OBJECTIVES: To comprehensively evaluate the validity of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for both prevalent diagnoses and less common diseases, and to assess the performance of a large language model (LLM)-based system in validating these codes. MATERIALS AND METHODS: This retrospective study analyzed hospital admissions from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. We developed a validated LLM-based system using GPT-4o, refined through iterative prompt engineering, to assess ICD-10-CM code validity. We measured the positive predictive value (PPV) of ICD-10-CM codes, PPV of principal and secondary diagnoses, and the performance of an LLM-based system in code validation. RESULTS: Among 865 079 assigned codes, the PPV was 84.6% (95% CI, 84.5%-84.6%). Principal diagnoses had a PPV of 93.9% (95% CI, 93.7%-94.1%), while secondary diagnoses had a PPV of 83.8% (95% CI, 83.7%-83.9%). The LLM system demonstrated high performance in validating ICD codes, achieving 93.6% accuracy, 95.4% sensitivity, and 85.2% specificity. Among correctly assigned secondary diagnoses, the majority (67.9%) represented historical or baseline conditions, while 32.1% reflected active conditions that deviated from baseline status; 22.3% of these emerged after hospital admission. PPV decreases with later diagnosis positions, with the largest decline occurring between principal and secondary diagnoses. DISCUSSION AND CONCLUSION: In this large-scale evaluation, ICD-10-CM codes exhibited generally high accuracy, though variability existed by position and condition type. A validated LLM system performed comparably to physician review and offers a scalable means to improve coding accuracy. These findings support the potential for integrating LLM-based auditing into routine workflows to strengthen the quality of administrative and research data.

In this article, a multiobjective optimization methodology is tailored for energy producers and sellers (Prosumers) and electric vehicle charging service providers (EVCS) in IES. The primary objective is to harmonize economic and safety aspects of system, while addressing uncertainties associated with renewable energy and involvement of electric vehicles (EVs) in integrated demand response (IDR). Initially, an orderly charging model for EVs is developed by driving behavior patterns, taking into account the influence of EVCS aggregating EVs in IDR via photovoltaic-energy storage charging stations. Subsequently, a stochastic programming method with conditional value at risk is utilized to alleviate potential risks from stochastic and intermittent nature of renewable energy outputs. Furthermore, the optimization framework incorporates operating costs of Prosumers and EVCS, and peak-to-valley difference of IES load as multiobjective functions. The optimal solution is determined by technique for order of preference by similarity to ideal solution. Ultimately, case studies confirm that the proposed approach effectively balances economic performance for multiple stakeholders and operational safety, and enhances system robustness under uncertain conditions.

This article investigates the asynchronous estimator design for the interval type-2interval type-2 (IT2) fuzzy Markov jump systems subject to dynamic quantization and deception attacks. From the perspective of the attacker, a novel protocol-based deception attackdeception attack (DA) strategy is proposed, which utilizes the information of quantized output to assess the importance degree of transmission signals. Furthermore, in order to conserve the limited energy of the adversary, the independent attack strategies are designed for different sensors. Besides, the hidden Markov modelhidden Markov model (HMM) is applied to observe the system mode. Employing the Lyapunov stability theory and linear matrix inequality method, the sufficient conditions are acquired to guarantee the strictly-dissipative performance of the estimation error. Finally, two examples are illustrated to confirm the efficacy of the designed estimator and the advantage of the proposed attack tactics.

This article aims to investigate the performance-guaranteed tracking problem for a class of uncertain nonlinear systems. The main goal is to attain control performance within prescribed-time (PT) limits despite the existence of mismatched disturbances. First, the mismatched disturbances are transformed into the equivalent forms. Second, for the unknown disturbance estimation, a PT extended state observer (PTESO) is developed to switch between the prescribed settling time ${\mathcal {T}}_{p}$ , the order is diminished to alleviate the occurrence of the peaking phenomenon. Furthermore, an ESO-based PT control strategy is constructed with time-varying gains. This allows real-time compensation of disturbances, and the prescribed performance is attained by virtue of a Lyapunov function employed combines both barrier and quadratic forms. Ultimately, the benefits and efficacy are illustrated via a numerical demonstration involving a wheeled mobile robot. The key features of this article include the observer capability to estimate unknown mismatching disturbances and the full effectiveness of the proposed controller for $t \in [t_{0}, \infty $ ), ensuring the convergence of tracking error to zero within any PT. Consequently, in addition to achieving the output tracking objective, the system can also exhibit favorable transient performance.

This article investigates secure bipartite containment control with the event-triggered mechanism (ETM) for nonlinear heterogeneous multiagent systems (MASs) via the feedback control in fixed time. A novel attacks detection algorithm and an updated label strategy are designed to judge the occurrence of Denial of Service (DoS) attacks or not. It can search for new multiple directed spanning trees for MASs with multiple leaders to decrease the negative influence when attacks happen. In this case, the dynamic ETM is adopted by adjusting the triggered threshold online to save resource consumption. The state-feedback control and output-feedback control are selectively employed according to the situation where the state value of the follower is available or not. The settling time is further relaxed by fixed-time stability theory and can be preset in advance. By theoretical discussion, conditions of achieving bipartite containment control are derived by Lyapunov functions. Finally, the effectiveness of the established control method is verified by simulations.

This article investigates the optimal bipartite consensus control (OBCC) problem for unknown second-order discrete-time multiagent systems (MASs). First, the coopetition network is constructed to describe the cooperative and competitive relationships between agents, and the OBCC problem is proposed by the tracking error and related performance index function. Based on the distributed policy gradient reinforcement learning (RL) theory, a data-driven distributed optimal control strategy is obtained to guarantee the bipartite consensus of all agents' position and velocity states. In addition, the offline data sets ensure the learning efficiency of the system. These data sets are generated by running the system in real time. Besides, the designed algorithm is an asynchronous version, which is essential to solve the challenge caused by the computational ability difference between nodes in MASs. Then, by means of the functional analysis and Lyapunov theory, the stability of the proposed MASs and the convergence of the learning process are analyzed. Furthermore, an actor-critic structure containing two neural networks is used to implement the proposed methods. Finally, a numerical simulation shows the effectiveness and validity of the results.

This article is concerned with the input and output event-triggered control simultaneously for uncertain Euler–Lagrange systems with input saturation via backstepping technology. An auxiliary dynamic system is designed to eliminate the effects of saturation. Fuzzy logic systems are applied to the state observer design, which enables the observer to obtain satisfactory observation results without relying on the system dynamic parameters. The event-triggered weight adaptive law is designed to alleviate system's computational burden. Due to the existence of output triggering, conventional recursive backstepping technology is infeasible as the virtual control law is discontinuous and no longer differentiable. Consequently, dynamic surface control is introduced to circumvent the aforementioned problem. Compared with the existing literature that considers output triggering, the assumption of the system function satisfying the global Lipschitz continuity condition is relaxed in this article. Besides, variable transformation technology is used to ensure that the output is constrained within asymmetric performance functions. Finally, the simulation results are depicted to verify the validity of the derived method.

In this paper, the time-varying formation tracking (TVFT) control problem is concerned for multi-agent systems (MASs). The primary objective is to achieve asymptotical convergence for formation tracking error subject to nonparametric and nonvanishing uncertainties. Firstly, in order to estimate lumped unmodeled dynamics and external disturbances, an event- triggered fuzzy extended state observer (FESO) inspired by our previous works is established for follower group, in which efficient nonlinear observation pattern and convenient linear numerical tractability are integrated. For rationally scheduling transmission, a dynamic event-triggered mechanism is applied to form adaptively regulating strategy. Secondly, a distributed TVFT control law is developed by utilizing neighborhood formation tracking errors. Different from conventional disturbance-tolerant methodologies, total disturbance compensation is introduced to attenuate uncertainty influence in real time. Consequently, active disturbance rejection formation tracking control architecture is systematically constructed for uncertain MASs. Moreover, considering switching topology, the controller design algorithm is presented by piecewise Lyapunov analysis. Finally, the effectiveness and advantages of the proposed event-triggered FESO-based active disturbance rejection control protocol is illustrated by an numerical example on unmanned aerial vehicle swarm system.

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi-Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.

This article tackles the problem of filtering design for continuous-time Roesser-type 2-D nonlinear systems via Takagi–Sugeno (T-S) fuzzy affine models. The aim is to design an admissible piecewise affine (PWA) filter such that the filtering error system is asymptotically stable with a prescribed disturbance attenuation level. First, 2-D Roesser nonlinear systems are approximated by a kind of 2-D fuzzy affine models with norm-bounded uncertainties. Then, the premise variable space of the 2-D fuzzy affine systems is partitioned into two classes of subspaces, that is: 1) crisp regions and 2) fuzzy regions. For each region, boundary continuity matrices and characterizing matrices are constructed by utilizing the space partition information and 2-D structure. After that, novel piecewise Lyapunov functions are constructed, based on which together with$S$-procedure, the asymptotic stability with$\mathcal H_{\infty }$performance is guaranteed for the filtering error system. By the projection lemma and some elegant convexification techniques, the PWA$\mathcal H_{\infty }$filtering design conditions are obtained. Finally, the less conservativeness and effectiveness of the proposed approach over a common Lyapunov function-based one are illustrated by simulation studies.

This article considers the problem of asynchronous fault detection filter (FDF) design for Takagi–Sugeno (T–S) fuzzy singular systems via dynamic event-triggered scheme. A mode-dependent dynamic event-triggered scheme is adopted to alleviate the communication load. Besides, a hidden Markov model is introduced to describe the asynchronous phenomenon between the system and the FDF. First, some sufficient criteria are established to ensure that the residual system is stochastically admissible with a certain$H_\infty$performance. Second, solvability criteria are presented to codesign the desired FDF gains and the event-triggered matrices. Finally, the correctness of the proposed method is shown by two examples.

This article addresses the aperiodic sampled-data control problem for flexible spacecraft with stochastic actuator failures. Flexible spacecraft dynamics are approximated by a group of T-S fuzzy models due to strong nonlinearity, and the multi-stochastic failures of spacecraft are depicted by a time-continuous and state-discrete Markov chain. To reduce the design conservativeness, a membership-sampling-dependent Lyapunov-Krasovskii functional (MSDLKF) is introduced to utilize the information of fuzzy membership functions and aperiodic sampling modes. Furthermore, a number of reliable fuzzy controllers are designed to obtain the exponential attitude stabilization under the circumstances of stochastic failures. At the same time, disturbance attenuation is ensured. The solution of the fuzzy controller gains can be obtained by solving a set of linear matrix inequalities (LMIs). In the end, an example of the practical flexible spacecraft system is given to illustrate the feasibility and validity of the proposed fuzzy control methods.

For the extended state observer (ESO) design, the most challenging issue is to exploit efficient nonlinear observation performance, while maintaining desirable linear numerical tractability under noncontinuous transmission. In this article, in order to address this issue, the sampled-data ESO design is investigated for a class of uncertain nonlinear systems. First, based on the fuzzy modeling approach with reasonable fuzzy rules and sets, the nonlinear functions of the ESO are suitably approximated by several local linear models weighted by membership functions. Then, a novel methodology of Takagi–Sugeno fuzzy extended state observer (TSFESO) is developed for the first time. By virtue of fuzzy membership functions, the estimating action is implemented in a nonlinear pattern, while taking advantages of Takagi–Sugeno fuzzy formulation, the observer gains can be calculated in a linear manner. As a result, the nonlinear estimating efficiency and linear numerical tractability are integrated in a unified framework. Second, for the aperiodic sampling case, the exponential convergence criterion for the TSFESO is presented by constructing a sampling- and time-dependent Lyapunov functional, in which the information on sampling action is taken into full consideration for desirable feasibility. Moreover, the observer design approach is also put forward. Finally, the superiorities and effectiveness of the proposed approaches are demonstrated by numerical examples.

This article is concerned with extended state observer (ESO) design for uncertain nonlinear systems. First, different from standard ESO exclusively applicable for integral chain systems, a ESO formation including nonlinear and linear types is proposed for general state-space models. Inspired by our previous work, a event-triggered generalized fuzzy ESO (GFESO) is developed. Second, in order to schedule transmission rationally, an improved total disturbance-resilient dynamic event-triggered mechanism (TDRDETM) is put forward. Third, under TDRDETM, the GFESO design approach is presented in sense of exponential convergence. Finally, numerical examples illustrate the effectiveness of the given methods.

In this article, the consensus problem of linear multiagent systems (MASs) is investigated, where an event-triggered mechanism combined with sampler is introduced to rationally utilize the network bandwidth and the communication energy. Different from most existing results on the event-triggered consensus of MASs, a time-varying communication delay (CD) with a more general form is constructed in this article. With the help of a new class of Lyapunov functional and advanced inequalities, some novel criteria for achieving the asymptotic consensus of the considered MASs under different CD cases are obtained. For demonstrating the superiority of the proposed method from the conservatism and the practicability, a compared example and an application of spacecraft formation flying are proposed, respectively.

In this paper, the consensus problem of linear discrete-time multi-agent systems is investigated. An event-triggered mechanism is introduced to rationally save the limited network resources, where the event-triggered condition of each agent only utilizes the information of itself and its neighbors. In the designed event-triggered mechanism, a novel dynamic threshold parameter adjusted with the system state fluctuations is constructed for screening the data more reasonably. With the help of Lyapunov stability theorem, the consensus conditions of the considered discrete-time multi-agent systems are obtained. To demonstrate the effectiveness and superiority of the proposed method, a numerical example is proposed.

In this article, the event-triggered asynchronous control problem of nonlinear multiagent systems is investigated based on Takagi-Sugeno fuzzy models. In order to rationally utilize network resources and elaborately avoid unnecessary continuous monitoring, an event-triggered mechanism with a new dynamic threshold parameter and a sampler are considered, respectively. Besides, an asynchronous operation method is adopted to deal with the mismatched premise variables between the fuzzy system and fuzzy controller. Based on the Lyapunov stability theory and appropriate inequality, some sufficient criteria in the form of linear matrix inequalities are obtained to ensure the stability of the closed-loop system. Finally, an illustrative example is provided to demonstrate the effectiveness and superiority of the proposed method.

This article is devoted to event-triggered stabilization for a class of interval type-2 (IT2) fuzzy systems with aperiodic sampling. First, the IT2 Takagi-Sugeno fuzzy model and sampled-data controllers are established subject to mismatched membership functions. Second, considering a nonuniform sampling case, a refined adaptive event-triggered communication scheme is proposed in a hierarchy form to dynamically adjust the direction and rate of the event-triggered threshold parameter by state changing trend and relative state error, respectively. Thus, a complete dual-directional regulating mechanism with sensitivity to state variation is reasonably created to give extra flexibility, which is beneficial for a preferable tradeoff between control performance and network resource. Third, considering the practical behaviors on the sampling interval, a novel integral type of time-dependent Lyapunov function is constructed. Then, the stability criterion and the controller design approach are derived. Finally, the numerical examples are provided to demonstrate the effectiveness and advantages of the proposed methods.

This article is concerned with the event-triggered guaranteed cost control for a class of Markovian jump systems with time-varying delays and partly unknown transition probabilities, which is described by the Takagi-Sugeno fuzzy model. For the sake of saving network bandwidth, an event-triggered mechanism related to system modes is developed in the feedback channel, in which network-induced delay randomly occurs. The stability criterion for the system with a guaranteed cost index is derived by the Lyapunov-Krasovskii functional. Moreover, sufficient conditions that ensure the existence of the admissible fuzzy controllers are given. By means of the proposed approach, the fuzzy control gains and event-triggered parameters can be codesigned. Finally, some simulation results are presented to demonstrate the superiority and effectiveness of the developed method.

This article is concerned with the issues of finite-time control for a class of continuous-time nonlinear semi-Markovian jump systems (SMJSs) with saturation constraint via observed-based control. Both sensor and actuator saturations, and unknown nonlinearities are considered simultaneously. The main purpose of this article is to derive parameter selection sufficient conditions by designing an observed-based controller. These conditions ensure that the system is finite-time boundedness (FTB). By employing some reasonable assumptions and constructing an appropriate semi-Markovian Lyapunov function, some novel finite-time stabilization criteria are obtained, which guarantee the FTB for the underlying systems over the whole finite-time interval. Meanwhile, the observed-based controller is designed. Finally, a practical example is provided to illustrate the effectiveness and merits of the proposed methods.

This paper is devoted to the stability and stabilization for Takagi-Sugeno fuzzy systems with time-varying delays. First, an improved matrix inequality is presented to bound both strictly and nonstrictly proper rational functions, which is more general than the existing versions of reciprocally convex lemmas. Second, by suitable operations on parameter-dependent polynomial multiplied by state rate, a couple of novel intermediary-polynomial-based functions (IPFs) are developed in delay-product types. Benefitting from slack matrices of IPFs, a certain degree of flexibility is furnished. More importantly than that, by feat of adjustment of the variable parameter, the resulting conditions will be further endowed with additive freedom, which relaxes the feasible space in a distinctive manner. Third, by utilizing IPFs along with triple integrals, the stability criteria and the controller design approach are derived by some advanced integral inequalities. Resorting to elaborate construction of IPFs, the strengths of bounding techniques are sufficiently exploited, and the information on delay derivative is adequately reflected. Consequently, more desirable performances are achieved, while without excessive computational complexity. Finally, the effectiveness of the proposed methods is verified by numerical examples.

This brief is concerned with stability analysis for delayed neural networks (DNNs). By establishing polynomials and introducing slack variables reasonably, some improved delay-product type of auxiliary polynomial-based functions (APFs) is developed to exploit additional degrees of freedom and more information on extra states. Then, by constructing Lyapunov-Krasovskii functional using APFs and integrals of quadratic forms with high order scalar functions, a novel stability criterion is derived for DNNs, in which the benefits of the improved inequalities are fully integrated and the information on delay and its derivative is well reflected. By virtue of the advantages of APFs, more desirable performance is achieved through the proposed approach, which is demonstrated by the numerical examples.

In this brief, by constructing an augmented Lyapunov-Krasovskii functional in a triple integral form, the stability analysis of delayed neural networks is investigated. In order to exploit more accurate bounds for the derivatives of triple integrals, new double integral inequalities are developed, which include some recently introduced estimation techniques as special cases. The information on the activation function is taken into full consideration. Taking advantages of the proposed inequalities, the stability criteria with less conservatism are derived. The improvement of the obtained approaches is verified by numerical examples.

This correspondence is concerned with the stability for interval time-delay systems. Triple integral inequalities without slack variables are proposed, which show more and more evident improvements as the degrees of intermediate polynomials increase. Driven by the proposed inequalities, the Lyapunov-Krasovskii functional with quadruple integrals and augmented vector consisting of triple integrals is constructed to derive less conservative stability criteria for time-delay systems. The numerical examples are provided to demonstrate the effectiveness of the proposed approaches.