Feature selection can select important features to address dimensional curses. Subspace learning, a widely used dimensionality reduction method, can project the original data into a low-dimensional space. However, the low-dimensional representation is often transformed back into the original space, resulting in information loss. In addition, gate function-based methods in Takagi–Sugeno–Kang fuzzy system (TSK-FS) are commonly less discrimination. To address these issues, this article proposes a novel feature selection method that integrates subspace learning with TSK-FS. Specifically, a projection matrix is used to fit the intrinsic low-dimensional representation. Subsequently, the low-dimensional representation is fed to TSK-FS to measure its availability. The firing strength is slacked so that TSK-FS is not limited by numerical underflow. Finally, the$\ell _{2,1}$-norm is introduced to select significant features and the connection to related works is discussed. The proposed method is evaluated against six state-of-the-art methods on 17 datasets, and the results demonstrate the superiority of the proposed method.

A novel observer-based fuzzy adaptive fault-tolerant control approach is proposed for multi-motor synchronously driving servo systems in the situation of a single-motor malfunction. Different from most existing fault-tolerant control strategies based on analytical redundancy, this paper leverages inherent hardware advantages of multi-motor systems and develops a structural redundancy-based fault-tolerant control by directly turning off the faulty motor, which enhances the reliability of the remaining healthy motors driving the asymmetric system. To further address the unknown nonlinearities and measurement inaccuracies introduced by the unbalanced faulty system, a fuzzy logic system is developed to identify the state-based unbalanced torque and additional friction dynamics, and a state observer is then designed to estimate the load speed. Moreover, the command-filtered backstepping technique is utilized in the control design process to reduce the computational complexity caused by the repeated signal derivations. After stability analysis, an experiment on the multi-motor prototype is implemented to show the effectiveness of the designed fault-tolerant control approach.

Graph convolutional neural networks (GCNs) have demonstrated effectiveness in processing graph structure. Due to the diversity and complexity of real-world graph data, heterogeneous GCN have attracted significant attention. However, existing research predominantly relies on explicit connections to explore graph heterogeneity. In the case of edgeless graphs, such as information systems, the absence of direct edges poses a significant challenge for employing GCNs to analyze the latent heterogeneity within these graphs. Traditional approaches overlook the topological features of information systems, resulting in information loss. This article introduces a heterogeneous graph convolutional neural network based on shadowed deviation relationship (SDHGCN) to investigate the heterogeneity of information systems, thereby improving the generalizability of heterogeneous GCNs. First, shadow deviation relationship and attribute deviation relationship are constructed derived from shadow sets and information gain, respectively. Then, dexterously integrated with the feature matrix of the information system (the relationship between objects and attributes), a highly expressive heterogeneous graph is constructed. Second, by performing graph convolution operations on the heterogeneous graph, effective node representations can be obtained to complete node classification tasks. Finally, the effectiveness and nonrandomness of SDHGCN are validated by extensive comparison and ablation experiments.

In the era of Big Data, the integration and fusion of heterogeneous information have become essential for addressing complex decision-making challenges in large-scale group decision-making (LSGDM), where expanding scale and diverse participant behaviors increasingly demand advanced techniques to enhance efficiency, fairness, and accuracy. To meet these demands, this article proposes a novel framework that leverages information-fusion principles to optimize group decision-making processes. First, an enhanced fuzzy C-means algorithm, termed Trust-based regularized fuzzy C-means, is introduced. By incorporating a regularization term derived from trust relationships, it improves clustering precision and enables finer segmentation of decision-makers (DMs), thus laying solid groundwork for consensus. Building on this, we develop an optimization model integrating three-way decision theory to resolve asynchronous differences among experts. The model uses dynamic trust updates to guide DMs from the negative region toward those in the positive region, with special emphasis on rapid consensus in emergencies. Furthermore, the study, for the first time, systematically investigates manipulative and herd behaviors within large datasets; adopting a cautious strategy to mitigate their impact on fairness and effectiveness, it embeds pseudotrust identification into the framework to further refine the decision environment. Extensive case studies and comparative experiments demonstrate that the proposed method significantly improves decision-making efficiency, fairness, and accuracy, offering new perspectives and practical tools for managing complex information and optimizing group behaviors in LSGDM.

In this paper, we propose a novel center-based clustering algorithm based on bidirectional conical information granularity. The main purpose is to fully absorb the semantic information of the ordinal relationship between objects to improve the performance of central clustering in identifying interleaved and imbalanced data. The proposed algorithm includes two main stages: (i) the stage of determining the cluster center and (ii) the division stage. In the stage of determining the cluster center, the first cluster center is determined by using the number of conical information granularity in the data, and the remaining cluster centers are determined by defining the statistical measure of “fuzzy importance degree”. In the division stage, we divide the points to be clustered into stable and active areas. The former quickly and accurately identifies and assigns the objects belonging to a cluster by measuring the fuzzy similarity between the objects to be clustered and the cluster center, and the latter assigns the objects in the active area by using the information of the points already assigned. This method describes the position and sorting relationship of objects that are granulated through ordinal relationships more accurately in the global environment, thereby gaining a more comprehensive understanding of the structural characteristics of the data. This helps to improve the accuracy and stability of clustering algorithms in handling interleaved and imbalanced data. This paper uses three clustering validity indicators to test the performance of our algorithm. We compare the results with those of six different types of popular clustering algorithms and new algorithms proposed in recent years. The experimental results show that the algorithm proposed in this paper can identify clusters more accurately on the datasets with a complex and staggered distribution. It is significantly better than the clustering algorithm participating in the comparison and has good robustness on datasets with added noise.

The control design method for a class of non-strict feedback nonlinear systems is studied in this brief considering uncertain nonlinearities and unknown non-symmetrical input dead-zone. Combining with the finite-time command filtered backstepping (FCFB) technique, a novel finite-time adaptive control approach is proposed in which a neural network-based methodology is adopted to cope with the uncertain nonlinearities in the non-strict feedback form. The input dead-zone model is transformed into a simple linear system with unknown gain and bounded disturbance which is estimated by an adaptive factor. Using the finite-time Lyapunov theory, the system convergence is proved. And the effectiveness of the proposed control scheme is verified through comparative numerical simulations.

In this article, we discuss fast finite-time consensus (FFTC) problems for uncertain nonlinear multiple mechanical systems with actuator faults and time-varying asymmetric output constraints. In order to guarantee the constraints are satisfied, an appropriate nonlinear mapping (NM) is employed to transform the original system with output constrains into an corresponding unconstrained one. Combining neural networks technology, graph theory, fast finite-time control theory, and backstepping technology, the actuator faults are considered to propose a distributed adaptive finite-time consensus (FTC) protocol, which can guarantee the position errors as well as the velocity errors reaching a region in finite time. Finally, an illustrative example is presented to support the obtained theoretical results.

Campus networks are widely deployed for organizations like universities and large companies. Applications and network-based services require campus networks to guarantee short queue and provide low latency and large bandwidth. However, the widely adopted packet-loss-based congestion control mechanism in client hosts builds up long queues in the switch buffer, which is prone to packet loss in burst scenarios, resulting in great network delay. Therefore, a scheme for efficiently controlling queue length of shallow buffer switches in campus networks is urgently needed. Explicit Congestion Notification(ECN) as an explicit feedback mechanism is widely adopted in data center networks to build lossless networks. In this paper, we propose Clean, an efficient queue length control scheme based on transparent ECN-proxy for campus networks. Clean is able to exert fine-grained control over arbitrary client TCP stacks by enforcing per-flow congestion control in the access point(AP). It allows the campus network switches to maintain a low queue length, resulting in high throughput, low latency and zero packet loss. Evaluation results demonstrate that Clean reduces the maximum queue length of the switch by 86% and reduces the 99th percentile latency by 85%. Clean also achieves zero packet loss in burst scenarios.

This paper investigates the adaptive practical finite-time stabilization for a class of nonstrict feedback nonlinear systems. The nonlinear systems under consideration contain unknown nonlinearities and control coefficients, and unknown deadzone and saturation input nonlinearities. Without imposing any conditions on the unknown nonlinearities, neural networks are utilized as the approximators to cope with these unknown nonlinear functions. The adding a power integrator technique is employed to construct controller and adaptive laws. The stability of the corresponding closed-loop system is proved with the help of the finite-time Lyapunov theory. Finally, two simulation examples are provided to show the validity of the proposed design method.

The notion of homomorphism, as an important tool for studying the relationship between information systems, has attracted a great deal of attention in recent years, and the authors tend to pay their attention to static information systems in the exis