The real-time and reliable transmission of data packets is a critical foundation for ensuring Internet of Things applications. Therefore, how to ensure the timely reliability of wireless sensor networks has become a hotspot. However, existing timely reliability models often overlook the impacts of energy depletion and channel capacity randomness on wireless transmission. Additionally, most evaluations focus on single-hop, single-path scenarios, while practical data transmission typically requires multi-hop and multi-path strategies. To overcome the above shortcomings, this study conducts the timely reliability evaluation and optimization of wireless sensor networks considering channel capacity randomness and energy depletion. First, focusing on data transmission delay modeling, this study emphasizes the effects of energy depletion and channel capacity randomness on wireless data transmission, and further proposes a timely reliability evaluation model based on the G/G/1 queuing model. Secondly, to tackle the computational challenges of multi-hop and multi-path data transmission, this study proposes a timely reliability solving algorithm that integrates the binary decision diagrams with Monte Carlo simulation.. Building on these foundations, this study develops a periodic optimization model for signal transmission power, balancing sensor lifetime and network transmission performance. Finally, taking the military Internet as an example, the effectiveness of the proposed method is verified.

In this article, to exclusively conquer detection degradation of benthonic organisms due to domain shifting between training and testing scenarios, an innovative domain-adaptive detection scheme, termed DAD-ULC, is holistically invented by uniformising light field and color distribution. To that end, the encoder-decoder domain converter (EDDC) with residual connection is created, such that samples in degraded domains can be transformed into a unified domain. The underwater light field perception loss (ULFPL) is further conceptualized by virtue of a multiscale Gaussian filter, so as to directly expedite light-field conversion, getting rid of benthonic organism structure information, thereby facilitating light-domain adaptation. By exploiting the similarity between generated and referenced images in Lab space, a color distribution consistency loss (CDCL) is empowered for color-distribution transfer. Eventually, the DAD-ULC scheme is established in an end-to-end manner by integrating with EDDC, ULFPL, and CDCL modules, thereby enabling identical light-color domains between training and testing samples. Comprehensive experiments and comparisons conducted on detecting underwater objects (DUOs) and URPC2020 datasets sufficiently demonstrate effectiveness and superiority in diversified domain-shifting challenges.

Vehicle trajectory is crucial for autonomous driving. Relatively scattered trajectory data points pose difficulties in modeling the motion’s inherent continuity in spatial and temporal dimensions. Additionally, identifying the driving patterns of vehicles from trajectories is also a significant challenge. These implicit characteristic patterns are difficult to discern from the complex details of the trajectory data. To address these issues, we propose a new framework called Wavelet-Mamba Trajectory Prediction (WMTP), which fuses wavelet analysis through state-space modeling to capture global trends in driving patterns and details of vehicle motion. The approach employs the Discrete Wavelet Transform (DWT) to decompose trajectory data into wavelet coefficients in different time scales and frequencies, and then utilizes these coefficients to generate the trajectory through the Inverse Discrete Wavelet Transform (IDWT). An encoder-decoder neural architecture is proposed for learning potential temporal features from the input trajectory sequences, and these features are projected into the wavelet domain. Wavelet coefficients of future trajectories are generated using different scale-oriented decoders. The estimated coefficients are further used to realize the trajectory prediction via the IDWT module. Experiments demonstrate that WMTP exhibits excellent performance on three large-scale real-world trajectory prediction datasets, with promising robustness and inference speed. The research findings also verify the effectiveness of time - frequency analysis in trajectory prediction tasks.

Due to water-surface reflection, wake and sun glitter, an uncrewed waterborne vehicle (UWV) faces a long-standing challenge in identifying water-surface obstacles especially with small-scale appearance. In this paper, inspired by the encoder-decoder architecture, a real-time surface obstacle segmentation network (RSOS-Net) is created to enable online surface-obstacle detection for a UWV. Primarily, the improved lightweight feature pyramid network structure is deployed to flexibly accommodate significant scale-variations and enhance focus on small obstacles, simultaneously. To address visual ambiguities caused by water-surface disturbances, the fast pyramid pooling module (FPPM) and attention-based feature fusion module (AFFM) are holistically devised within lightweight encoder and decoder, respectively. Accordingly, the FPPM is able to distinguish obstacles from sun glitters by capturing both local and global contextual information via cascaded pooling, while the AFFM can rule out reflections by virtue of channel-spatial attention mechanism augmenting detailed features and spatial locations. Results show that the RSOS-Net achieves an F1 score of 65.1% on the LaRS dataset, while the detection speed reaches 79.5 frames per second on an NVIDIA RTX 3060 platform. Notably, the RSOS-Net secured first place in the 3rd USV-based Embedded Obstacle Segmentation Challenge, with official results available athttps://macvi.org/workshop/macvi25/summary

The detection of small surface targets plays a critical role in maritime search and rescue (SAR) operations, ensuring the safety of people and property at sea. This paper provides a comprehensive review of the latest advancements and research in small sea surface target detection for maritime SAR missions. Deep learning-based models facilitate accurate target detection and localization by transforming image or video frames into high-dimensional abstract representations, enabling effective detection in complex sea surface environments. However, challenges such as occlusion, blurring, and reflections on the sea surface significantly complicate small target detection. To address these challenges, this paper summarizes a range of effective approaches, including context information, multi-scale learning, anchor-free detection, super-resolution, attention mechanisms, and sample-oriented approaches. These approaches aim to enhance the performance of small target detection in applications such as uncrewed aerial vehicles (UAV) and uncrewed supply vessels. Furthermore, this paper classifies small target datasets, providing a detailed overview based on their collection methods and application scenarios, while highlighting representative datasets. Through a thorough analysis of both methodologies and datasets, this paper offers valuable insights and directions for the future development of small target detection technology in maritime search and rescue operations.

In this article, to augment benthonic organism features and suppress underwater background noises, simultaneously, a fuzzy channel–spatial attention-based benthonic organism detection (FCSA-BOD) scheme is proposed. Main contributions are as follows: 1) with the aid of spatial global average and maximum pooling, fuzzy channel attention (FCA) is originated to adaptively recalibrate channel responses by fusing discriminative and textural channel attention maps, which are derived from two independent single-hidden-layer feedforward networks, such that benthonic organism and background feature maps can be strengthened and suppressed, respectively; 2) by exploiting channel global average and maximum pooling, fuzzy spatial attention (FSA) is created to highlight spatial regions associated with benthonic organisms by fusing multiple spatial attention maps possessing completely different receptive fields, such that different-scale benthonic organism features on the same feature map can be significantly augmented, simultaneously; and 3) the FCSA-BOD scheme is eventually established in a modular manner by integrating FCA and FSA modules within a deep learning framework. Comprehensive experiments demonstrate that the proposed FCSA-BOD scheme outperforms state-of-the-art underwater detection approaches.

This article studies the attack detection and reconstruction problem for autonomous underwater vehicles (AUVs) subject to unknown but bounded disturbances and measurement noise. First, a Takagi-Sugeno (T-S) fuzzy model is utilized to address the nonlinearity of AUVs. Then, a$H_{\infty }$T-N-L observer is introduced to estimate system state. Subsequently, to improve the accuracy of attack detection, an improved attack detection method within the T-S fuzzy system framework is obtained by the reachability analysis of the residual. Afterwards, the effect of disturbances and measurement noise on attack reconstruction and isolation is analyzed. The accuracy of attack reconstruction and isolation for T-S fuzzy systems is improved by zonotopic analysis. Finally, the effectiveness and advantages of the proposed method are verified through simulation results.

Satellite-terrestrial networks (STNs) are a promising paradigm for providing Internet services for users globally. Since the dynamics of service resources and the uncertainty of computational requests, how the service resources in STNs can be efficiently exploited to execute differentiated computational tasks is an essential challenge. In this work, we investigate the dynamic task scheduling in the integrated cloud-edge STNs. First, we propose a cloud-edge collaborative computing framework in STNs, where the computational tasks of users can be processed collaboratively by satellite edge servers, terrestrial edge servers, and cloud servers. Based on this framework, a dynamic task scheduling problem is formulated with the objective of maximizing the task success rate. Second, to make effective real-time decisions at decision points in the dynamic scheduling process, we develop a scheduling heuristic with the routing rule and queuing rule, which incorporates dynamic features related to servers, computational tasks, and network environments. Third, to automatically learn the scheduling heuristic, we propose a multitree genetic programming with rule reconstruction (MTGPRR), which introduces a selective reconstruction operator. This operator increases the chance of matching good rules with other rules by recombining common individuals and elites. Experimental results demonstrate that the proposed MTGPRR performs significantly better than the state-of-the-art methods in improving the task success rate. Moreover, the evolved scheduling heuristic has good interpretability, which is important for practical applications.

This article studies the problem of identifying both stealthy and nonstealthy integrity attacks for unknown linear discrete-time systems. By establishing a necessary and sufficient condition that characterizes stealthy and nonstealthy attacks in a sparse subspace-based form, the considered problem is transformed into the decoupled identification of two subsignals: 1) the subsignal in attack-identifiable space; and 2) the subsignal in attack-unidentifiable space. A novel complex field encoding scheme, through which the two subsignals are projected into different attack-identifiable subspaces, is then proposed so that the two subsignals get identified independently by subspace projection technique. Compared with the previous results that are limited to the identification of either sparse sensor attacks or stealthy ones, the restriction on sparsity is removed and the identification of stealthy attacks is unaffected by nonstealthy ones. Finally, the effectiveness of the proposed approach is illustrated by two simulation examples.

If underactuated dynamics can not be accommodated in path planning for an uncrewed surface vehicle (USV), sway unactuation makes the path untrackable, thereby threatening navigation resilience. In this paper, an underactuated navigation actor-critic (UNAC) deep reinforcement learning (DRL) framework is devoted to feasibly trackable path planner for an underactuated USV. By integrating a long short-term memory module into the critic network, historical state sequences are compressed into low-dimensional representations, thereby balancing optimization efficiency and complexity. To incrementally optimize pertinent path, a composite reward function covering process, collision and target approaching is created to fertilize the optimizer. Within the algorithmic flow, successive waypoints-tracking mechanism is embedded, ensuring that path-planning policy can be compatible with unactuated sway dynamics. To provide sufficiently diversified learning scenarios that can hardly experience in practice, Unity3D-based virtual-reality environments are established by replicating real-world shallow and congested situations, showcasing that the UNAC-based path planner works resiliently under unfamiliar circumstances. Compared to conventional DRL methods, the UNAC-DRL framework not only accelerates the learning process but also achieves a success rate improvement of up to 15%.

Aiming at autonomous massive transportation by sea, economically condition-based cooperative control solution remains unrevealed and is highly desirable for collective swarming of distributed unmanned surface vehicles (USVs) suffering from narrow-band communication and unstructured unknowns. In this paper, an event-triggered self-organizing swarm control (ESSC) scheme is innovated to flexibly helm a herd of USVs, and features main contributions as follows: 1) A suite of self-organizing swarm mechanism consisting of aggregation, collision avoidance and heading alignment is holistically established, such that emerging behaviors of swarm kinetics can be self-evolved for flexible morphology; 2) Within adaptive dynamic programming framework, an event-triggered optimal solution to USV swarm control is worked out by deriving optimization-oriented event-triggering mechanism from swarm kinetics tracking errors, thereby making a rational balance between channel occupation and tracking accuracy; and 3) Approximately optimal control actions are acquired by employing actor-critic reinforcement learning networks to solve Hamilton-Jacobi-Bellman equation, thereby assuring communication parsimony and control optimality, simultaneously. Performance validations with intensive comparisons to time-triggered methods demonstrate the effectiveness and superiority in terms of tracking accuracy, channel occupancy and control optimality, in addition that extensive application to roundup scenario showcases the proposed ESSC scheme performs feasible extension to wide-range tasks.

LiDAR-dominated simultaneous localization and mapping (SLAM) for uncrewed surface vehicles in a harbor is challenged by sparse water-surface returns and motion-induced distortions. This paper presents a direct registration LiDAR-inertial odometry and mapping (DR-LIOM) framework that incorporates inertial measurement unit (IMU) pre-integration for point cloud de-skewing, keyframe-based local map construction, and full-resolution point-to-plane iterative closest point for robust odometry estimation. Loop closure mechanism and multi-source factor graph, integrating LiDAR, IMU, RTK/GPS, and loop constraints, are devised to ensure globally consistent mapping. Field experiments in Linghai Harbor demonstrate that the DR-LIOM achieves absolute trajectory errors around 0.5 m in 2D and 3D domains, significantly outperforming typical SLAM baselines that suffer from localization errors in tens of meters. Despite using dense registration, the DR-LIOM not only maintains real-time feasibility at 12.3 Hz, but also supports integration of surface and underwater data, enabling high-fidelity harbor charting.

There has been increasing attention to the maintenance optimization of balanced systems in recent years. However, existing studies mostly neglect state-dependent inspection intervals and group maintenance, which inadequately addresses the maintenance challenges of balanced systems. Thus, we propose an availability evaluation and maintenance optimization method for balanced systems considering state-dependent inspection intervals. First, multiple maintenance thresholds are introduced to characterize the maintenance strategy considering preventive maintenance and state-dependent inspection intervals, where the next inspection interval is determined based on the post-maintenance system state. Then, the system availability is evaluated by combining semi-regenerative theory and universal generating functions, where calculations are simplified by merging the same system states. Meanwhile, this study also explores the system availability under group maintenance to better reflect reality. Second, the average maintenance cost per unit of time is calculated using the renewal theory. The optimal maintenance thresholds are given by minimizing the maintenance cost under the constraint of minimum system availability. To improve the optimization efficiency, a tabu list-based two-stage iterative partial optimization algorithm is proposed. Finally, the effectiveness of the proposed method is demonstrated through a numerical example involving a lithium-ion battery pack.

This study addresses a tampered-data recovery problem for linear discrete-time systems with completely unknown system dynamics under stealthy attacks. The basic idea is to identify the stealthy attack, that lies in any of attack-stealthy subspaces, and compensate for it. Different from the existing sparse recovery methods which are applicable to nonstealthy sparse attacks, a novel encoding scheme, where a set of subdecoding matrices is designed specifically for each 1-D attack-stealthy subspace, is developed so that the parameters of the stealthy attack can be identified via a subspace projection technique. A necessary and sufficient condition of determining the targeted subspace by using n parallel attack identification filters is established for this encoding scheme. Especially, a composite encoding matrix characterizes the lower and upper boundaries of the recovery error covariance's trace. A simulation example of a flight vehicle illustrates the efficiency of the proposed approach.

This article presents a prescribed-time tracking control method for underactuated unmanned surface vessels (USVs) using a neural reinforcement learning (RL) approach. First, the hand position approach, addressing the underactuated characteristic, is employed to convert the model of USV into the integral cascade form. Second, inheriting the advantages of prescribed performance control (PPC), the proposed controller not only stabilizes the tracking error within an asymmetric prescribed-time range, but also removes the limitation of initial conditions. Subsequently, the identifier—actor-critic architecture is introduced in the optimized backstepping design, which gives the solution of the Hamilton–Jacobi–Bellman (HJB) equation. Meanwhile, the relative threshold event-triggered mechanism is also considered to reduce the communication burden and executive frequency of actuators. Finally, employing the Lyapunov stability theory, it is proven that all signals in the closed-loop system are bounded, and the developed control scheme is demonstrated to be effective through simulation and experimental results.

In this paper, a two-level visual servo strategy is elaborately devised for an unmanned surface vehicle (USV) equipped with a pan-tilt camera, so as to exactly track the desired trajectory around a visual target without direct position measurements. In the lower level, a barrier function-based adaptive pseudo-inverse (BFAP) controller is specially designed for the camera to keep the target in sight. Together with a finite-time position observer (FPO) and a finite-time extended state observer (FESO), a model-free finite-time trajectory-tracking control (MFTC) scheme is naturally synthesized for the USV on the higher level. Prominent advantages are presented as follows: 1) The BFAP controller can not only circumvent the singularity issue in a simpler manner, but also solve the field-of-view problem thoroughly in spite of unknown image depth; 2) The FPO provides a new vision-based method to locate the USV by rapidly calibrating a constant extrinsic parameter of the camera online, achieving higher positioning accuracy; and 3) The MFTC scheme allows all model information of the USV to be unknown, which is more favorable to practical implementations. Stability analyses are strictly made by the Lyapunov theory, and simulation studies conducted on the prototype CyberShip II comprehensively demonstrate remarkable performance of the proposed BFAP controller and MFTC scheme.

In this paper, aiming at a fleet of manned-unmanned surface vehicles (MUSVs), a novel visual servo swarming (VSS) mechanism is deliberately established by embodying visibility maintenance, swarm aggregation, collision avoidance and velocity matching. By making full use of line-of-sight ranges and angles between neighbors, a swarm of unmanned surface vehicles (USVs) with unknown inertia masses, internal dynamics and external disturbances can cooperate with a manned surface vehicle (MSV), thereby emerging flexibly collective behaviors in GPS-denied environments. To endow MUSVs with individually flexible behaviors, the VSS mechanism renders velocity matching of USVs with the MSV executing human-intelligence intention. Meanwhile, barrier Lyapunov functions are employed to reliably maintain visibility and avoid collisions among individuals, simultaneously. Distributed neural approximators using reduced-dimension inputs are devised to estimate unknown dynamics of USVs, while residual uncertainties are thoroughly suppressed by robust adaptations, thereby contributing to a model-free VSS (MVSS) scheme. Eventually, together with projection-based adaptive laws, the MVSS-based controller ensures uniform boundedness of estimation parameters and asymptotic convergence of regulation errors. Simulation results demonstrate remarkable efficacy in terms of collective performance.

There exist long-standing challenges in balancing scale and stability of an unmanned surface vehicle for search and rescue (SAR) missions. In this paper, a new wave-adaptive unmanned quadramaran (WUQ) with independent suspension mechanism is structurally designed by virtue of double-wishbone suspension (DWS) and quadruped buoyancy. By inventing DWS-based floating feet, wave-induced forces can be independently absorbed by dampers, thereby significantly attenuating vibrations of the main hull. By dynamics optimization, key parameters of damper springs are numerically determined. Furthermore, the reliable safety of suspension mechanism under various conditions is ensured by finite element analysis on the strength validation. Virtual prototype results show that suspension-damping objective of the main hull can be further promoted by$52.3\%$after fine optimization on damper springs, while key connecting parts of the suspension mechanism are fully able to afford sufficient stress under diversified conditions. Owe to remarkable stability and safety, the innovated WUQ would definitely fertilize smart SAR efficiency in maritime transportation.

For maritime autonomous surface ships (MASS), challenges exist in visual detection of occluded marine vessels since invisible occlusion is required to be inferred from locally unoccluded features which are weakly related to the entirety. In this paper, an attention-aware occlusion detection scheme of marine vessels, termed AodeMar, is originated from the viewpoint of MASS transportation. To this end, a position enhancement module is created by virtue of residual connections and coordinate attentions such that high-level semantics and spatial feature dependencies can be efficiently exploited, respectively, thereby accurately locating bounding boxes. Moreover, a multi-scale feature semantics correlation block is devised by combining spatial pyramid pooling and swin transformer-based self-attention encoder in order that the classification ability can be fertilized in both global and local sense. Experiments and comparisons show that the proposed AodeMar outperforms typical approaches including Faster R-CNN, SSD and YOLO series in terms of detection accuracy and robustness.

For maritime autonomous surface ships, challenges exist in visual detection of ships in sea foggy scenarios, thereby severely degrading visual detection autonomy. In this paper, math-data integrated defogging (MDD) mechanism is created within a ship detection network, termed MDD-ShipNet. Main contributions are as follows: 1) The MDD enhancement module (MDD-EM) is implemented by devising 5 filters, i.e., defog, exposure, tone, contrast and sharpen, as well as a CNN-based parameter learner, such that defogging enhancement can progressively be conducted in a transparent manner; 2) The detector is innovated by employing polarized self-attention (PSA) and weighted bidirectional feature pyramid network (WBiFPN), so as to preserve long-range dependencies and high-resolution channel-spatial features, simultaneously, thereby sufficiently fusing shallow and semantics information associated with contributions to detection; and 3) The entire MDD-ShipNet framework is ultimately established in a weakly supervised manner by integrating MDD-EM and PSA-WBiFPN-based detector, and is fertilized by hybrid dataset that is diversely contributed by real-world and synthesized sea-foggy images. Comprehensive experiments and comparisons eventually validate that the MDD-ShipNet framework outperforms typical approaches deriving from the detection after image enhancement, multi-task learning and domain adaption in terms of [email protected], [email protected]:.95 and FPS.

In this article, to exclusively suppress unuseful underwater noise feature and effectively avoid overenhancement, simultaneously, an underwater attentional generative adversarial network (UAGAN) is innovatively established. Main contributions are as follows: combining dense concatenation with global maximum and average pooling techniques, a cascade dense-channel attention (CDCA) module is devised to adaptively distinguish noise feature and recalibrate channel weight, simultaneously, such that low-contribution feature map can be effectively suppressed; to sufficiently capture long-range dependence between any two nonlocal spatial patches, the position attention (PA) module is created such that the deviation among independent patches can be sufficiently eliminated, thereby avoiding overenhancement; and in conjunction with CDCA and PA modules, the entire UAGAN framework is eventually developed in an end-to-end manner. Comprehensive experiments conducted on underwater image enhancement benchmark (UIEB) and underwater robot professional contest (URPC) datasets demonstrate remarkable effectiveness and superiority of the proposed UAGAN scheme by comparing with typical underwater image enhancement approaches including unsupervised color correction method, image blurriness and light absorption, underwater dark channel prior, underwater generative adversarial network, underwater convolutional neural network, and WaterNet in terms of peak signal-to-noise ratio, underwater color image quality evaluation, underwater image quality measures, etc.

This article reports our study on a reduced adaptive fuzzy decoupling control for our lower limb exoskeleton system which typically is a multi-input-multi-output (MIMO) uncertain nonlinear system. To show the applicability and generality of the proposed control methods, a more general MIMO uncertain nonlinear system model is considered. By decoupling control, the entire MIMO system is separated into several MISO subsystems. In our experiments, such a system may have problems (even unstable) if a traditional fuzzy approximator is used to estimate the complicated coupling terms. In this article, to overcome this problem, a reduced adaptive fuzzy system together with a compensation term is proposed. Compared to traditional approaches, the proposed fuzzy control approach can reduce possible chattering phenomena and achieve better control performance. By employing the proposed control scheme to an actual 2-DOF lower limb exoskeleton rehabilitation robot system, it can be seen from the experimental results that, as expected, it has good performance to track the model trajectory of a human walking gait. Therefore, it can be concluded that the developed approach is effective for the control of a lower limb exoskeleton system.

In this article, suffering from unmatched visual-servo uncertainties and unknown dynamics/disturbances, an extreme learning-based monocular visual-servo (ELMVS) scheme is developed for maneuvering an unmanned surface vessel (USV) to reach the desired pose. By virtue of the backstepping philosophy, complex visual-servo unknowns are elaborately encapsulated into lumped nonlinearities, which are further accurately accommodated by devising a single-hidden layer feedforward network based adaptive compensating identifier (SACI). Within the SACI architecture, hidden nodes are completely model free and are randomly generated without tedious learning, and thereby dramatically expediting fast-dynamics identification. Moreover, by exploiting approximation residuals, direct hyperbolic-tangent links between input and output layers are deployed to enhance identification accuracy. Eventually, the Lyapunov synthesis guarantees that the proposed ELMVS scheme can asymptotically render visual-servo errors arbitrarily small while target features can be kept within the field of view. Remarkable performance and superiority is finally demonstrated on a prototype USV.

An unmanned surface vehicle (USV) under complicated marine environments can hardly be modeled well such that model-based optimal control approaches become infeasible. In this article, a self-learning-based model-free solution only using input-output signals of the USV is innovatively provided. To this end, a data-driven performance-prescribed reinforcement learning control (DPRLC) scheme is created to pursue control optimality and prescribed tracking accuracy simultaneously. By devising state transformation with prescribed performance, constrained tracking errors are substantially converted into constraint-free stabilization of tracking errors with unknown dynamics. Reinforcement learning paradigm using neural network-based actor-critic learning framework is further deployed to directly optimize controller synthesis deduced from the Bellman error formulation such that transformed tracking errors evolve a data-driven optimal controller. Theoretical analysis eventually ensures that the entire DPRLC scheme can guarantee prescribed tracking accuracy, subject to optimal cost. Both simulations and virtual-reality experiments demonstrate the remarkable effectiveness and superiority of the proposed DPRLC scheme.

In this article, a novel reinforcement learning-based optimal tracking control (RLOTC) scheme is established for an unmanned surface vehicle (USV) in the presence of complex unknowns, including dead-zone input nonlinearities, system dynamics, and disturbances. To be specific, dead-zone nonlinearities are decoupled to be input-dependent sloped controls and unknown biases that are encapsulated into lumped unknowns within tracking error dynamics. Neural network (NN) approximators are further deployed to adaptively identify complex unknowns and facilitate a Hamilton-Jacobi-Bellman (HJB) equation that formulates optimal tracking. In order to derive a practically optimal solution, an actor-critic reinforcement learning framework is built by employing adaptive NN identifiers to recursively approximate the total optimal policy and cost function. Eventually, theoretical analysis shows that the entire RLOTC scheme can render tracking errors that converge to an arbitrarily small neighborhood of the origin, subject to optimal cost. Simulation results and comprehensive comparisons on a prototype USV demonstrate remarkable effectiveness and superiority.

In this paper, the event-triggered consensus problem of linear multiagent systems with time-varying communication delays is addressed. Different from the existing event-triggered consensus results with communication delays, more general nonuniform time-varying communication delays are considered. To avoid the asynchronous phenomenon caused by nonuniform delays, a novel periodic switching controller is developed. Based on this controller, the resulting consensus error system can be modeled as a periodic switching system. Furthermore, the exponential stability of the consensus error system is derived by utilizing the Lyapunov approach and the dwell-time analysis method. Finally, an illustrative example is presented to demonstrate the effectiveness of the developed method.

In this article, in the presence of unknown actuator faults, input saturations, and complete unknowns including both internal dynamics and external disturbances, exact trajectory-tracking problem of a surface vehicle (SV) is solved by creating a finite-time fault estimator based fault-tolerance control (FFE-FTC) scheme. By virtue of input saturations, smoothly saturated controls are separated from input nonlinearities including unknown faults, thereby leaving faults-mixed unknowns be exactly observed by a finite-time fault estimator (FFE). By defining an integral sliding-mode (ISM) manifold and deriving affine controls with unknown gains from smooth saturations, Nussbaum technique is deployed to synthesize a uniformly adaptive finite-time controller working in a large range outside input saturations. Within the close range where saturations are removed, an ISM-based nonsmooth controller with finite-time auxiliary compensation dynamics is devised to finely stick tracking errors to the origin. Intuitively, large- and close-range control actions are triggered by measuring the ISM error, thereby contributing to the entire FFE-FTC scheme, which achieves exact fault-tolerance and unknown rejection under input saturations. Lyapunov and nonsmooth syntheses prove that the closed-loop FFE-FTC system is globally finite-time stable. Simulation results and comparisons on a prototype SV demonstrate remarkable performance in terms of feasibly saturated controls and exact trajectory-tracking, simultaneously.

In marine guard, patrol, and racing scenarios, it is of great importance to autonomously helm an underactuated surface vehicle (USV) to accurately achieve successive waypoints tracking (SWT) with prescribed velocities and courses. In this paper, in the presence of completely unknown dynamics and environmental forces, the emerging SWT problem is innovatively solved by creating a novel model-free guidance-control integrated framework. In lieu of direct guidance to the waypoint which inevitably suffers from singularity, a new tool called bridge trajectory (BT) exactly passing through the generalized waypoint (GW) is first developed by defining marching and ahead points, i.e., a marching point (MP) and an ahead point (AP). Combining with pursuit guidance and finite-time unknown observer (FUO), successive BTs are switched ON and OFF, with the aid of MP and AP, respectively. By virtue of the FUO, cascade analysis, filtered backstepping, and Lyapunov approach, BT tracking control laws for surge and yaw motions are further synthesized to ensure successive GWs with desired positions, velocities, and courses can be tracked accurately, and thereby eventually contributing to a BT-guided model-free solution to the SWT problem. Simulation studies on a benchmark USV demonstrate remarkable performance of the proposed method.

Kernel recursive least squares (KRLS) is a kind of kernel methods, which has attracted wide attention in the research of time series online prediction. It has low computational complexity and updates in a recursive form. However, as data size increases, computational complexity of calculating kernel inverse matrix will raise. And it has some difficulties in accommodating time-varying environments. Therefore, we have presented an improved KRLS algorithm for multivariate chaotic time series online prediction. Approximate linear dependency, dynamic adjustment, and coherence criterion are combined with quantization to form our improved KRLS algorithm. In the process of online prediction, it can bring computational efficiency up and adjust weights adaptively in time-varying environments. Moreover, Lorenz chaotic time series, El Nino-Southern Oscillation indexes chaotic time series, yearly sunspots and runoff of the Yellow River chaotic time series online prediction are presented to prove the effectiveness of our proposed algorithm.

For a class of large-scale nonlinear systems in nonstrict-feedback structure with immeasurable states, an adaptive decentralized fuzzy control strategy on the basis of event-triggered mechanism is investigated in this paper. Fuzzy logic systems are implemented to construct an observer, which approximates the unknown nonlinear function in the controller. In light of backstepping control technique and event-triggered mechanism, a decentralized adaptive fuzzy control approach is proposed to compensate for the effects of actuator faults. When the triggering condition is satisfied, the communication burden can be reduced. Moreover, the whole signals of the closed-loop system are semiglobally uniformly ultimately bounded and Zeno behavior can be successfully excluded. Furthermore, the outputs of subsystems can track the desired reference signals. Finally, some simulation results are utilized to testify the effectiveness of the proposed control scheme.

In this paper, suffering from both complex uncertainties and underactuations, accurate trajectory tracking control problem of an asymmetric underactuated surface vehicle (AUSV) is first addressed by guiding yaw dynamics which are free of persistent excitation (PE). Using nested coordinate transformations, the AUSV is formulated in a cascade structure consisting of translation and rotation subsystems with complex uncertainties. Finite-time uncertainty observers (FUOs) are devised to exactly estimate transformed uncertainties, and enable separation principle in controller and observer syntheses. By virtue of creating yaw-guided dynamics, rotation tracking is shaped to stabilize yaw and sway tracking discrepancies, simultaneously, in collaboration with yaw controller. Nominal dynamics of translation tracking errors are globally asymptotically stabilized by surge-control synthesis using cascade analysis and Lyapunov approach, and thereby contributing to global asymptotic stability of the entire translation-rotation tracking system. Eventually, an FUO-based yaw-guided tracking control (FUO-YTC) scheme of an AUSV with complex uncertainties is established. Simulation studies demonstrate remarkable performance.

In this paper, a backpropagating constraints-based trajectory tracking control (BCTTC) scheme is addressed for trajectory tracking of a quadrotor with complex unknowns and cascade constraints arising from constrained actuator dynamics, including saturations and dead zones. The entire quadrotor system including actuator dynamics is decomposed into five cascade subsystems connected by intermediate saturated nonlinearities. By virtue of the cascade structure, backpropagating constraints (BCs) on intermediate signals are derived from constrained actuator dynamics suffering from nonreversible rotations and nonnegative squares of rotors, and decouple subsystems with saturated connections. Combining with sliding-mode errors, BC-based virtual controls are individually designed by addressing underactuation and cascade constraints. In order to remove smoothness requirements on intermediate controls, first-order filters are employed, and thereby contributing to backsteppinglike subcontrollers synthesizing in a recursive manner. Moreover, universal adaptive compensators are exclusively devised to dominate intermediate tracking residuals and complex unknowns. Eventually, the closed-loop BCTTC system stability can be ensured by the Lyapunov synthesis, and trajectory tracking errors can be made arbitrarily small. Simulation studies demonstrate the effectiveness and superiority of the proposed BCTTC scheme for a quadrotor with complex constrains and unknowns.

The leader-following consensus problem for linear multi-agent systems with matched unknown nonlinear and actuator faults under switching topology is addressed in this paper. The main contributions are as follows: (1) In contrast to the existing results under switching topology, the unknown nonlinear considered in this paper are completely unknown; (2) The developed controller is capable of compensating for the actuator faults and the nonlinear simultaneously. To be more specific, by approximating the nonlinear by a fuzzy logical system (FLS) and by introducing switching mechanism in the distributed controller and adaptive update laws, a new FLS-based distributed adaptive controller is developed. By virtue of estimating the norm of weight vector in the FLS, the developed controller can compensate for unknown nonlinear under the actuator faults. In addition, it is proven that the developed controller can guarantee that the consensus errors are uniformly ultimately bounded. An illustrative example demonstrates the effectiveness and efficiency of the proposed method.

In this paper, the bearing-only formation control problem of multi-agent systems is addressed. The main contributions of the paper are twofold: (1) The local maximal clique graph instead of the global infinitesimal bearing rigidity graph is used to describe the formation configuration; (2) The proposed formation control law is globally stable. To be more specific, by considering the inter-agent communication topology satisfies the maximal clique graph condition, a cost function is designed based on the target formation. Next, the rotation bias of the final formation and the target formation are analyzed. Based on the negative gradient of the cost function, a globally stable distributed controller that only depends on the inter-bearing measurements is proposed and global convergence result and analysis are given. An illustrative example demonstrates the effectiveness and efficiency of the proposed control law.

In this paper, an universal adaptive fuzzy control (UAFC) scheme using output tracking error is proposed for practical tracking control of a class of nonlinear systems with unmeasured states and completely unknown perturbed dynamics including unknown dynamics and/or external disturbances. A tracking error system with measurable output is first derived from the output tracking problem, and unmeasured states are observed by an universal fuzzy state observer (UFSO), whereby adaptive fuzzy approximators and an universal adaptive gain are employed to estimate unknown dynamics and dominant unknown residuals, respectively. In conjunction with the rescaled UFSO and observation errors, the UAFC using output tracking error feedback is explicitly constructed, in a recursive manner, by employing the command filtered backstepping technique, whereby intermediate virtual signals and their first derivatives associated with complex dynamics can be reconstructed by second-order filters. Furthermore, adaptive mechanisms for fuzzy approximators and the universal gain pertaining to the UFSO and UAFC are derived from the Lyapunov synthesis. Theoretical analysis proves that all signals of the closed-loop system are bounded and the output tracking error and observation error can converge to an arbitrarily small region determined by a prescribed accuracy. Simulation results demonstrate the effectiveness and superiority of the proposed UAFC scheme.

Motivated by the challenging difficulty in tracking an uncertain marine vehicle (MV) with unknown dynamics and disturbances to any unmeasurable/unknown trajectory, which is unresolved, an adaptive universe-based fuzzy control (AUFC) scheme with retractable fuzzy partitioning (RFP) in global universe of discourse (UoD) is created to achieve global asymptotic model-free trajectory-independent tracking. By defining an error surface and intensively exploring the MV structure, tracking error dynamics are sufficiently trimmed via separating external unknowns including trajectory dynamics and disturbances from internal nonlinearities dependent on tracking errors. An innovative retractable fuzzy approximator (RFA) using the RFP is developed to estimate internal nonlinearities and does not require a priori knowledge on the UoD, thereby contributing to a globally adaptive approximation based control approach in conjunction with Lyapunov synthesis. Together with RFA residuals, external unknowns are globally dominated by adaptive universal compensators driven by tracking error surface. Eventually, tracking errors and their derivatives globally asymptotically converge to the origin and all other signals of the closed-loop system are bounded. Simulation studies demonstrate superior performance of the proposed AUFC scheme in terms of both tracking and approximation.

Precise real-time tidal prediction is essential for management of marine activities. Note that the tidal change is a complex time-varying nonlinear process, which is not only generated by periodic configurations of celestial bodies but also influenced by various time-varying meteorological factors. To achieve precise real-time tidal prediction, an ensemble tidal prediction mechanism is established by combining harmonic analysis and variable neural networks which are constructed by discrete wavelet transform (DWT). In the ensemble prediction mechanism, a conventional harmonic analysis method is used for representing the effects of celestial factors, while a DWT-based variable neural network is used for representing the nonlinear time-varying influences of meteorological factors and other unmodeled factors. The decomposition of tidal residual time series enables the precise prediction of time-varying dynamics by using a variable neural network whose dimensions and parameters are both adaptively tuned online. High accuracy of the proposed ensemble real-time tidal prediction mechanism is demonstrated by simulation studies on the actual tidal measurements collected from the Old Port Tampa tidal station and other four tidal stations in the USA.

In this paper, for a general class of uncertain nonlinear (cascade) systems, including unknown dynamics, which are not feedback linearizable and cannot be solved by existing approaches, an innovative adaptive approximation-based regulation control (AARC) scheme is developed. Within the framework of adding a power integrator (API), by deriving adaptive laws for output weights and prediction error compensation pertaining to single-hidden-layer feedforward network (SLFN) from the Lyapunov synthesis, a series of SLFN-based approximators are explicitly constructed to exactly dominate completely unknown dynamics. By the virtue of significant advancements on the API technique, an adaptive API methodology is eventually established in combination with SLFN-based adaptive approximators, and it contributes to a recursive mechanism for the AARC scheme. As a consequence, the output regulation error can asymptotically converge to the origin, and all other signals of the closed-loop system are uniformly ultimately bounded. Simulation studies and comprehensive comparisons with backstepping- and API-based approaches demonstrate that the proposed AARC scheme achieves remarkable performance and superiority in dealing with unknown dynamics.

A ship roll prediction scheme is proposed using an adaptive sliding data window (SDW), which is designed to represent time-varying nonlinear dynamics of ship roll motion. The adjustment of SDW is realized by developing an improved fuzzy Gath-Geva (IFGG) segmentation approach, which detects the changes of system dynamics and thereby automatically adapting the scale of SDW. By virtue of the learning scheme with an adaptive SDW, the variable-structure radial basis function network is constructed sequentially to online predict ship roll dynamics. Experimental studies on online ship roll prediction are conducted on measured data from YuKun's full-scale sea trial. Results demonstrate the remarkable predictive accuracy of the proposed ship roll prediction model as well as the effectiveness of the IFGG-based SDW in terms of representing time-varying dynamics.

In this paper the formation control problem of heterogeneous multi-agent systems is investigated. The formation control problem is first transformed to an output regulation problem, and then a distributed adaptive control law based on state feedback is designed to solve the problem. The salient feature of the developed control law is that the feedback gains are independent of the Laplacian matrix of the underlying system topology, which is of global nature. Furthermore, it is shown that all agents can form a formation and keep a desired relative position to a leader under a necessary and sufficient condition, and all feedback gains will approach some constant as time goes to infinity. An example demonstrates that the proposed control law is highly effective and efficient.

Sentiment classification has been a very hot topic in the field of natural language processing (NLP) and understanding in recent years. Recurrent neural networks (RNN) is a widely used tool to deal with the classification problem of variable-length sentences. The standard RNN can only access the preceding context of a sentence. In this paper, a new architecture termed Comprehensive Attention Recurrent Neural Networks (CA-RNN) which can store preceding, succeeding and local contexts of any position in a sequence is developed. The bidirectional recurrent neural networks (BRNN) is used to access the past and future information while a convolutional layer is employed to capture local information. The standard RNN is also replaced by two recently emerged RNN variants, namely long short-term memory (LSTM) and gated recurrent unit (GRU), to enhance the effectiveness of the new architecture. Another salient feature of the proposed model is that it can be trained end-to-end without any human intervention. It is very easy to be implemented. We conduct experiments on several sentiment-labeled datasets and analysis tasks. Experiment results demonstrate that capturing comprehensive contextual information can significantly enhance the classification accuracy compared with the standard recurrent models and the new models can achieve competitive performance compared with the state-of-the-art approaches.

Classification involves the learning of the mapping function that associates input samples to corresponding target label. There are two major categories of classification problems: Single-label classification and Multi-label classification. Traditional binary and multi-class classifications are sub-categories of single-label classification. Several classifiers are developed for binary, multi-class and multi-label classification problems, but there are no classifiers available in the literature capable of performing all three types of classification. In this paper, a novel online universal classifier capable of performing all the three types of classification is proposed. Being a high speed online classifier, the proposed technique can be applied to streaming data applications. The performance of the developed classifier is evaluated using datasets from binary, multi-class and multi-label problems. The results obtained are compared with state-of-the-art techniques from each of the classification types.

It is well known that the architecture of the extreme learning machine (ELM) significantly affects its performance and how to determine a suitable set of hidden neurons is recognized as a key issue to some extent. The leave-one-out cross-validation (LOO-CV) is usually used to select a model with good generalization performance among potential candidates. The primary reason for using the LOO-CV is that it is unbiased and reliable as long as similar distribution exists in the training and testing data. However, the LOO-CV has rarely been implemented in practice because of its notorious slow execution speed. In this paper, an efficient LOO-CV formula and an efficient LOO-CV-based ELM (ELOO-ELM) algorithm are proposed. The proposed ELOO-ELM algorithm can achieve fast learning speed similar to the original ELM without compromising the reliability feature of the LOO-CV. Furthermore, minimal user intervention is required for the ELOO-ELM, thus it can be easily adopted by nonexperts and implemented in automation processes. Experimentation studies on benchmark datasets demonstrate that the proposed ELOO-ELM algorithm can achieve good generalization with limited user intervention while retaining the efficiency feature.

In this paper, a novel adaptive robust online constructive fuzzy control (AR-OCFC) scheme, employing an online constructive fuzzy approximator (OCFA), to deal with tracking surface vehicles with uncertainties and unknown disturbances is proposed. Significant contributions of this paper are as follows: 1) unlike previous self-organizing fuzzy neural networks, the OCFA employs decoupled distance measure to dynamically allocate discriminable and sparse fuzzy sets in each dimension and is able to parsimoniously self-construct high interpretable T-S fuzzy rules; 2) an OCFA-based dominant adaptive controller (DAC) is designed by employing the improved projection-based adaptive laws derived from the Lyapunov synthesis which can guarantee reasonable fuzzy partitions; 3) closed-loop system stability and robustness are ensured by stable cancelation and decoupled adaptive compensation, respectively, thereby contributing to an auxiliary robust controller (ARC); and 4) global asymptotic closed-loop system can be guaranteed by AR-OCFC consisting of DAC and ARC and all signals are bounded. Simulation studies and comprehensive comparisons with state-of-the-arts fixed- and dynamic-structure adaptive control schemes demonstrate superior performance of the AR-OCFC in terms of tracking and approximation accuracy.

In this paper, an extreme learning control (ELC) framework using the single-hidden-layer feedforward network (SLFN) with random hidden nodes for tracking an unmanned surface vehicle suffering from unknown dynamics and external disturbances is proposed. By combining tracking errors with derivatives, an error surface and transformed states are defined to encapsulate unknown dynamics and disturbances into a lumped vector field of transformed states. The lumped nonlinearity is further identified accurately by an extreme-learning-machine-based SLFN approximator which does not require a priori system knowledge nor tuning input weights. Only output weights of the SLFN need to be updated by adaptive projection-based laws derived from the Lyapunov approach. Moreover, an error compensator is incorporated to suppress approximation residuals, and thereby contributing to the robustness and global asymptotic stability of the closed-loop ELC system. Simulation studies and comprehensive comparisons demonstrate that the ELC framework achieves high accuracy in both tracking and approximation.

This paper presents a closed-loop trajectory tracking controller for an Unmanned Underwater Vehicle(UUV) with five degrees of freedom. A backstepping control (BSC) methodology combined with Lyapunov theorem is adopted to design the controller of trajectory tracking. Then an online-tuning fuzzy neural network (FNN) framework is chosen to inherit the conventional BSC law. Moreover, the adaptive parameters tuning laws are derived in the sense of Lyapunov stability theorem and projection algorithm to ensure the network convergence as well as stable control performance. Finally, the simulation results on UUV verify that an excellent performance of the proposed controller can be obtained.

In this paper, an observer-based adaptive control scheme for a class of nonlinear systems with parametric uncertainties is proposed. The adaptive observers using parameter estimates ensure the identification errors of system states are convergent to zero, and force the parameter estimates approach to the true values especially if the observer gains are selected large enough. By combining the Lyapunov synthesis with backstepping framework, the global asymptotical stability and bounded signals of the resulting closed-loop system can be ensured. A numerical example is employed to demonstrate the effectiveness of the proposed adaptive control scheme.

In this paper, the motion dynamics of a large tanker is modeled by the generalized ellipsoidal function-based fuzzy neural network (GEBF-FNN). The reference model of tanker motion dynamics in the form of nonlinear difference equations is established to generate training data samples for the GEBF-FNN algorithm which begins with no hidden neuron. In the sequel, fuzzy rules associated with the GEBF-FNN-based model can be online self-constructed by generation criteria and parameter estimation, and can dynamically capture essential motion dynamics of the large tanker with high prediction accuracy. Simulation studies and comprehensive comparisons are conducted on typical zig-zag maneuvers with moderate and extreme steering, and demonstrate that the GEBF-FNN-based model of tanker motion dynamics achieves superior performance in terms of both approximation and prediction.

This paper deals with tanker steering control based on a novel multiple-input multiple-output generalized ellipsoidal-basis-function-based fuzzy neural network (GEBF-FNN) with online updating of system structure and parameters. The main contributions of this paper are as follows. 1) A GEBF-FNN-based nonlinear steering model incorporating the nonlinearity underlying tanker dynamics is proposed. 2) The static local controller (SLC), whose controller gains are locally fixed with the initial forward speed and the desired heading for individual steering commands, is implemented. 3) The dynamic local controller (DLC) is further realized by employing adaptive controller gains pertaining to time-varying forward speed and heading dynamics. 4) The GEBF-FNN-based steering controller is developed by identifying a nonlinear mapping from the heading error, acceleration and forward speed to dynamic controller gains, and thereby contributing to a model-free adaptive control scheme. Simulation results and comprehensive studies on benchmark problems demonstrate that the GEBF-FNN-based model can capture the essential tanker dynamics, and the proposed SLC, DLC, and GEBF-FNN-based schemes achieve superior performance in terms of heading regulation and forward speed loss. In comparison with the SLC and traditional fuzzy controllers, the DLC and GEBF-FNN-based controllers achieve higher accuracy of heading regulation with less rudder efforts and minimal forward speed losses.

In this paper, traditional single-hidden layer feedforward network (SLFN) is extended to novel generalized SLFN (GSLFN) by employing polynomial functions of inputs as output weights connecting randomly generated hidden units with corresponding output nodes. The significant contributions of this paper are as follows: 1) a primal GSLFN (P-GSLFN) is implemented using randomly generated hidden nodes and polynomial output weights whereby the regression matrix is augmented by full or partial input variables and only polynomial coefficients are to be estimated; 2) a simplified GSLFN (S-GSLFN) is realized by decomposing the polynomial output weights of the P-GSLFN into randomly generated polynomial nodes and tunable output weights; 3) both P- and S-GSLFN are able to achieve universal approximation if the output weights are tuned by ridge regression estimators; and 4) by virtue of the developed batch and online sequential ridge ELM (BR-ELM and OSR-ELM) learning algorithms, high performance of the proposed GSLFNs in terms of generalization and learning speed is guaranteed. Comprehensive simulation studies and comparisons with standard SLFNs are carried out on real-world regression benchmark data sets. Simulation results demonstrate that the innovative GSLFNs using BR-ELM and OSR-ELM are superior to standard SLFNs in terms of accuracy, training speed, and structure compactness.

A novel meta-cognitive-based scaffolding classifier, namely Generic-Classifier (gClass), is proposed in this paper to handle non-stationary classification problems in the single-pass learning mode. Meta-cognitive learning is a breakthrough in the machine learning where the learning process is not only directed to craft learning strategies to exacerbate the classification rates, i.e., how-to-leam aspect, but also is focused to accommodate the emotional reasoning and commonsense of human being in terms of what-to-leam and when-to-learn facets. The crux of gClass is to synergize the scaffolding learning concept, which constitutes a well-known tutoring theory in the psychological literatures, in the how-to-learn context of meta-cognitive learning, in order to boost the learner's performance in dealing with complex data. A comprehensive empirical studies in time-varying datasets is carried out, where gClass numerical results are benchmarked with other state-of-the-art classifiers. gClass is, generally speaking, capable of delivering the most encouraging numerical results where a trade-off between predictive accuracy and classifier's complexity can be achieved.

In this paper, an adaptive robust dynamic constructive fuzzy neural control (AR-DCFNC) scheme for trajectory tracking of a surface vehicle with uncertainties and unknown time-varying disturbances is proposed. System uncertainties and unknown dynamics are identified online by a dynamic constructive fuzzy neural network (DCFNN) which is implemented by employing dynamically constructive fuzzy rules according to the structure learning criteria. The entire AR-DCFNC system is globally asymptotical stable.

Extreme Learning Machine (ELM) is a special single-hidden-layer feedforward neural networks with very fast learning speed and has attracted significant research attentions in recent years. The salient feature of ELM is that the input parameters can be randomly generated instead of being exhaustively tuned, and thus saving a great deal of computational expenses. However, the architecture of ELM has a great impact on its generalization performance and is traditionally determined by a trial and error manner. Therefore selecting an appropriate ELM architecture becomes the crucial problem in the successful application of ELM. In this paper, we propose a Robust Incremental ELM (RI-ELM), a constructive method where the hidden nodes are added one by one. We consider RI-ELM as a robust algorithm, because the suitable architecture is selected based on the Leave-One-Out (LOO) Cross-Validation procedure, a nearly unbiased and reliable criterion, but with notorious slow implementation speed. To tackle this speed issue, we propose an efficient formula that can incrementally update the LOO error with every new hidden node recruited, thus RI-ELM can secure the speed advantage of ELM and achieve good and robust performance. Furthermore, RI-ELM requires nearly zero user intervention since the architecture is automatically determined.

Artificial Neural Networks (ANN) is a major machine learning technique inspired by biological neural networks. However, the process of its parameter tuning is usually tedious and time consuming, and thus it becomes a major bottleneck for it being efficiently applied and used by nonexperts. In this paper, a novel ANN algorithm, termed as Automatic Regularized Extreme Learning Machine (AR-ELM), based on a Regularized Extreme Learning Machine (RELM) using ridge regression is proposed. It is a true automatic ANN learning algorithm in the sense that it can automatically identify the appropriate essential system parameter according to the input data without the need of user intervention. Since this method is based on a relatively straightforward formula, it can achieve very fast learning speed. The simulation results shows that the proposed AR-ELM algorithm can achieve comparable results to tedious cross-validation tuned RELM. Furthermore, we also systematically investigate one of the biggest concerns of ELM, its randomness nature, caused by randomly generated parameters.

In this paper, a vessel maneuvering model (VMM) based on multi-output dynamic radial-basis-function network (MDRBFN) is proposed. Data samples used for training and testing are obtained from the vessel maneuvering dynamics based on a group of nonlinear differential equations. In order to identify the vessel maneuvering model, the differential equations are transformed into nonlinear state-space form. Considering that the desired states are not only dependent on system inputs, i е., rudder defection and propeller revolution, but also previous states, the proposed MDRBFN is focus on the multi-input multi-output (MIMO) case. The structure of traditional fixed-size RBF networks is difficult to determine, so the growing and pruning algorithm is introduced to multi-output RBF networks to realize RBF networks with dynamic structure. The MDRBFN starts with no hidden neurons, and during the learning process, hidden neurons are recruited automatically according to hidden nodes generation criteria and parameters estimation. In addition, insignificant hidden nodes would be deleted if the node significance is lower than the predefined threshold. As a consequence, the proposed MDRBFN-based VMM (MDRBFN-VMM) reasonably captures the essential maneuvering dynamics with a compact structure. Finally, simulation results indicate that the proposed MDRBFN-VMM achieves promising performance in terms of approximation and prediction.

In this paper, an adaptive self-constructing RBF neural control (AS-RBFNC) scheme for trajectory tracking of MIMO uncertain nonlinear systems with unknown time-varying disturbances is proposed. System uncertainties and unknown dynamics can be exactly identified online by a self-constructing RBF neural network (SC-RBFNN) which is implemented by employing dynamically constructive hidden nodes according to the structure learning criteria including hidden node generating and pruning. The globally asymptotical stability of the entire AS-RBFNC control system is derived from Lyapunov approach.

Novel constructive and destructive parsimonious extreme learning machines (CP- and DP-ELM) are proposed in this paper. By virtue of the proposed ELMs, parsimonious structure and excellent generalization of multiinput-multioutput single hidden-layer feedforward networks (SLFNs) are obtained. The proposed ELMs are developed by innovative decomposition of the recursive orthogonal least squares procedure into sequential partial orthogonalization (SPO). The salient features of the proposed approaches are as follows: 1) Initial hidden nodes are randomly generated by the ELM methodology and recursively orthogonalized into an upper triangular matrix with dramatic reduction in matrix size; 2) the constructive SPO in the CP-ELM focuses on the partial matrix with the subcolumn of the selected regressor including nonzeros as the first column while the destructive SPO in the DP-ELM operates on the partial matrix including elements determined by the removed regressor; 3) termination criteria for CP- and DP-ELM are simplified by the additional residual error reduction method; and 4) the output weights of the SLFN need not be solved in the model selection procedure and is derived from the final upper triangular equation by backward substitution. Both single- and multi-output real-world regression data sets are used to verify the effectiveness and superiority of the CP- and DP-ELM in terms of parsimonious architecture and generalization accuracy. Innovative applications to nonlinear time-series modeling demonstrate superior identification results.

In this paper, an online self-organizing scheme for Parsimonious and Accurate Fuzzy Neural Networks (PAFNN), and a novel structure learning algorithm incorporating a pruning strategy into novel growth criteria are presented. The proposed growing procedure without pruning not only simplifies the online learning process but also facilitates the formation of a more parsimonious fuzzy neural network. By virtue of optimal parameter identification, high performance and accuracy can be obtained. The learning phase of the PAFNN involves two stages, namely structure learning and parameter learning. In structure learning, the PAFNN starts with no hidden neurons and parsimoniously generates new hidden units according to the proposed growth criteria as learning proceeds. In parameter learning, parameters in premises and consequents of fuzzy rules, regardless of whether they are newly created or already in existence, are updated by the extended Kalman filter (EKF) method and the linear least squares (LLS) algorithm, respectively. This parameter adjustment paradigm enables optimization of parameters in each learning epoch so that high performance can be achieved. The effectiveness and superiority of the PAFNN paradigm are demonstrated by comparing the proposed method with state-of-the-art methods. Simulation results on various benchmark problems in the areas of function approximation, nonlinear dynamic system identification and chaotic time-series prediction demonstrate that the proposed PAFNN algorithm can achieve more parsimonious network structure, higher approximation accuracy and better generalization simultaneously.

We have revealed the analytical structures and stability analysis of the three-dimensional fuzzy controllers involving trapezoidal input fuzzy sets, singleton output fuzzy sets, Zadeh fuzzy AND triangular norm, Zadeh fuzzy OR triangular co-norm, Mamdani inference method and centroid defuzzification algorithm. This class of fuzzy controllers is a combination of a nonlinear PID controller with dynamic proportional gain, dynamic integral gain and dynamic derivative gain plus a piecewise constant term. Based on the mathematical structures, the bounded-input bounded-output (BIBO) stability conditions for fuzzy control systems have been obtained by the well-known small gain theorem. A computer simulation is provided to illustrate that the new fuzzy controller is effective and superior to the conventional PID controller.

By summarizing the common characteristics of popular triangular and trapezoidal fuzzy sets, the more extensive generalized trapezoid-shaped (GTS) fuzzy set has been proposed. We have contributed to the analytical structures and stability analysis of the simplest two-dimensional fuzzy controllers using GTS input fuzzy sets. This class of fuzzy controllers is a combination of a piecewise linear PI controller plus a piecewise constant term. Based on the mathematical structures, the bounded-input bounded-output (BIBO) stability conditions for fuzzy control systems have been obtained by the well-known Small Gain Theorem. Two computer simulations are provided to demonstrate that the new fuzzy controller is effective and superior to the conventional PID controller.