This paper proposes a two-stage method to identify parameters in nonlinear dynamical systems like the FitzHugh-Nagumo model. An initial coarse screening using Kolmogorov-Arnold Networks (KANs) with segmented Huber fitting is refined by a physics-based solver using a sliding-window strategy. The method demonstrates strong robustness under rigorous stress tests, outperforming Kalman-based baselines with lower error metrics. For instance, its accuracy remained high (R2 > 0.97) even as noise intensity increased by three orders of magnitude; in this case, the RMSE increased only modestly, from 0.0028 to 0.0983. In conclusion, the validation of this method for complex system identification not only highlights its potential where traditional approaches may fail, but also lays a critical foundation for advancing AI applications in parameter estimation.

This work addresses the passivity disturbance analysis for delayed discrete-time systems under quantized event-triggered communication and deception attacks. To ease network load and minimize communication overhead, an event-triggered mechanism and a logarithmic quantizer are introduced individually. The influences of event-triggered, quantization, stochastic terms, and cyber attacks are combined within a single unified framework and by designing a suitable Lyapunov–Krasovskii functional (LKF), we establish sufficient conditions that ensure asymptotic stability of the system. The proposed method derives stability and passivity conditions that allow larger admissible delays and stronger tolerance to disturbances, leading to improved control performance. Building on these results, a resilient event-triggered control approach is formulated using linear matrix inequalities (LMIs). In addition, when external disturbances occur, further conditions are derived to guarantee system passivity. In the end, the proposed method demonstrates its effectiveness through four numerical simulation examples, which include comparative studies that assess control accuracy, achieving an approximately 83

This paper examines the control problem of stochastic neutraltype systems (SNTSs) that experience parameter uncertainties, mixed delays, external disturbances, and Levy noises under Markovian switching. To address this problem, an appropriate Lyapunov-Krasovskii functional (LKF) is formulated, and the generalized Ito formula is used in conjunction with inequality techniques to establish exponential stability and guarantee H infinity performance. The primary objective is to achieve exponential stabilization of SNTSs using a sampled-data sliding mode control (SDSMC) strategy designed using linear matrix inequalities (LMIs). An integral sliding surface is introduced, and the corresponding equivalent control is derived to ensure the stability of the systems. Subsequently, a sliding mode control law was developed to guarantee both the existence of the sliding mode and the reachability of the switching surface. Finally, numerical simulations are presented to demonstrate the validity and robustness of the proposed control approach.

This work investigates the problem of finite-time boundedness (FTB) of interconnected systems with time delays in the interconnections, using sliding mode control (SMC) along with a partitioned interval strategy. The finite-time interval [0, T] is divided into two sub-intervals: [0, T] and [T, T], where T < T. A novel sliding mode surface (SMS) is designed, and an equivalent control is derived from the SMS. Furthermore, an SMC with a reaching law is designed to counteract disturbances and ensure that the system reaches the SMS within a finite-time. By utilizing this property, the SMC with the reaching law is applied to the first sub interval [0, T], while the equivalent control derived from the SMS is applied to maintain the states within the SMS for the remaining interval [T, T]. Moreover, by considering a Lyapunov-Krasovskii functional, sufficient conditions are derived in the form of linear matrix inequalities to guarantee the H infinity FTB of the system. Finally, two examples are given to demonstrate the effectiveness of the proposed SMC and sufficient conditions, with simulation results confirming the FTB of the proposed model.

This paper investigates the existence and stability of a fractional-order labor dynamics model formulated using the fractional Nabla difference operator, specifically the Atangana-Baleanu fractional derivative in the Caputo sense. The model incorporates fractional dynamics to capture memory effects and the complex interactions associated with workforce layoffs. First, we present the mathematical formulation of the system and discuss its relevance to labor dynamics. Using the fixedpoint theory, we establish the existence and uniqueness of solutions, demonstrating that the system is well posed. Furthermore, we examine the stability of the model in the Mittag-Leffler-Hyers-Ulam sense, providing insight into its long-term qualitative behavior. Numerical simulations support the theoretical findings and demonstrate that the fractional-order parameter significantly influences the system's dynamics. Overall, this study offers a more general and flexible framework for modeling layoff processes using fractional calculus.

This paper investigates the finite-time HPo synchronization (HPo FTS) problem for nonlinear time-varying systems (NTVSs) subject to time-varying delays and external disturbances. The interval matrix method (IMM) is introduced, and its definitions, properties, and geometric interpretations are established to address the challenges induced by time-varying coefficients. Based on this framework, sufficient HPo FTS conditions are derived in the form of solvable linear matrix inequalities (LMIs). A novel control strategy is proposed, where the online state-dependent switching PI controllers (SDSPICs) are designed and tuned using a global search algorithm (GSA)-based optimization scheme. This strategy switches between proportional-integral and proportional modes to enhance convergence performance while reducing control energy cost. Simulation results validate the theoretical analysis and demonstrate the superior performance of the proposed SDSPIC compared with the traditional PI controller.

This paper investigates the joint design of fault detection (FD) filter and controller for discrete-time networked unmanned aerial vehicle (UAV) systems operating under event-triggered communication mechanism (ETCM) and stochastic deception attacks. The discrete-time networked UAV control system model is formulated. The novel FD filter is proposed by considering the ETCM with network-induced delays and stochastic deception attacks following the Bernoulli distribution. Meanwhile, the fault weighting matrix is incorporated into the filter design to enhance the fault detection (FD) performance. In light of the stochastic analysis method and the introduction of new relaxation matrices, the joint design methodology of FD filter and controller is proposed, under which the event-triggered residual system is guaranteed to be asymptotically stable while satisfying the prescribed H∞ performance index. Finally, numerical simulation examples involving biased fault and noise-type fault under different attack rates are provided to prove the effectiveness of the proposed results.

This study investigates the implementation of sampled-data control techniques in positive Markov jump systems (PMJSs), aiming to ensure exponential stability in the mean and a predefined ℒ_1 -gain performance. Initially, we develop a sampled-data model for PMJSs and examine its mean stability characteristics. Subsequently, sufficient criteria are derived to guarantee that the system achieves the targeted ℒ_1 -gain performance, with particular emphasis on how the sampling period influences system dynamics. These criteria are utilized to formulate a sampled-data controller, with its gains determined using a linear programming method. The proposed methodology’s effectiveness is confirmed through numerical simulations, which support the theoretical results.

In this paper, we investigate the global asymptotic stability of complex-valued neural networks (CVNNs) subject to time-varying delays and parameter uncertainties. We establish novel stability conditions that guarantee both the existence and uniqueness of equilibrium states, as well as the global convergence of the network trajectories. By constructing a suitable Lyapunov-Krasovskii functional, the approach inherently accounts for the stability of CVNNs subject to time-varying delays. Finally, numerical examples are presented to verify the theoretical findings, illustrating both the effectiveness and the practical applicability of the proposed approach.

This study examines a Caputo-type fractional-order food chain model, considering the Holling type II functional response with the vigilance effect. The model explores the interaction dynamics of the food chain model, which consists of prey, middle predators, and top predators. Additionally, habitat complexity is integrated into the model, which is assumed to reduce predation rates by lowering the encounter rates between predators and prey. All possible feasible equilibrium points are determined and the stability of our proposed model is explored near the equilibrium points. To support the analytical findings, numerical simulation results are given in terms of time series, phase portraits, and bifurcation diagrams. It is discovered that the proposed model can become more stable under a fractional-order derivative. Moreover, the interplay between the vigilance effect and habitat complexity is shown to influence the existence of stable and periodic dynamics.

Epidemic modeling plays a crucial role in understanding disease transmission and informing public health strategies. This study presents a fractional Susceptible-Exposed-Infected-Quarantined-Recovered (SEIQR) model incorporating Atangana–Baleanu-Caputo (ABC) fractional derivatives to capture memory effects in disease dynamics. The model extends classical ordinary differential equation-based frameworks by integrating a fractional approach, enhancing its applicability to real-world epidemic scenarios. A key feature of our model is the inclusion of mortality rates across all disease compartments, providing a refined representation of influenza-like infections with pandemic potential. We conduct a detailed stability analysis to assess equilibrium states and derive conditions for disease control. Numerical simulations further validate the theoretical findings, offering insights into epidemic progression and intervention strategies. Our results highlight the significance of fractional calculus in epidemiological modeling and its potential to improve predictive accuracy for infectious disease outbreaks.

This paper focuses on an innovative passivity analysis of wind turbine systems employing a decentralized event-triggered control approach. To establish new stability conditions, a novel Lyapunov-Krasovskii functional (LKF) is introduced, incorporating information from both the upper and lower bounds of the interval time-varying delay. We provide a new approach to identify the time instants of transmission from the sensors to the controller using just locally available information in event-triggered communication schemes. By using the relaxed integral inequality and the delay decomposition methodology, novel delay-dependent criteria guarantee that the considered system is passive. The passification problem is then handled by designing a decentralized event-triggered controller based on the acquired passivity conditions. The established stability conditions are expressed as linear matrix inequalities (LMIs), which may be numerically verified using the MATLAB LMI toolkit. Finally, a numerical example clearly demonstrates the superiority of our theoretical results using the simulation model.

In this paper, a recurrent intermittent control (RIC) for the synchronization of fractional-order chaotic neural networks (FOCNNs) is proposed in view of the extended dissipativity-based approach. Successively, standard linear matrix inequalites (LMIs)based extended dissipative criteria are derived through differential inclusions and inequality mechanisms. Several sufficient conditions are obtained to ensure the synchronization of FOCNNs. Furthermore, RIC is generated to solve the synchronization problem for the considered FOCNNs. Based on the piecewise Lyapunov functional, this paper derives a exponentially stable criterion in connection with linear matrix inequalities using the Matlab toolbox. Extended dissipativity can be employed to precisely define L2-L infinity, H infinity, passivity, and (Q, S, R)-& vartheta; dissipative performance. This is achieved by modifying the weighting matrices to achieve the desired performance level. The successful application of the stability criterion that was planned is demonstrated by the outcomes of the simulation.

This study employs specific and appropriate criteria to investigate the global stability of hybrid bidirectional associative memory (BAM) neural networks with time delays. We establish new and more general conditions for global asymptotic robust stability (GARS) in time-delayed BAM neural networks at the equilibrium point. This represents the primary objective and novelty of this paper. The derived conditions are independent of the system parameter delay in BAM neural networks. Finally, we provide numerical examples to illustrate the applicability and effectiveness of our conclusions with respect to network parameters.

The present research investigates the global asymptotic stability of bidirectional associative memory (BAM) neural networks using distinct sufficient conditions. The primary objective of this study is to establish new generalized criteria for the global asymptotic robust stability of time-delayed BAM neural networks at the equilibrium point, utilizing the Frobenius norm and the positive symmetrical approach. The new sufficient conditions are derived with the help of the Lyapunov–Krasovskii functional and the Frobenius norm, which are important in deep learning for a variety of reasons. The derived conditions are not influenced by the system parameter delays of the BAM neural network. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed conclusions regarding network parameters.

In this work, we propose a ratio-dependent intraguild predation model that incorporates fear and gestation delay. Further, the cost of the intraguild predator's fear is thought to decrease the size of the intraguild prey. The interaction between prey and predator takes place in the form ratio-dependence type. This type of functional response offers a valuable perspective by considering the feeding rates based on the relative abundance of both prey and predators. We first determine the conditions under which positive equilibrium points exist, and then we examine the local stability properties of the equilibria. In order to gain insight into the rich dynamics of the proposed non-delayed model, the occurrence of Hopf-bifurcation with respect to the fear parameter near the interior equilibrium point is discussed. Furthermore, we evaluate the local stability and the possibility of a Hopf bifurcation for the delayed model. The direction and stability of the Hopf bifurcation are also studied using the center manifold theorem. Finally, we conduct the numerical simulations to demonstrate our analytical results. (c) 2025 L&H Scientific Publishing, LLC. All rights reserved.

This paper investigates the bifurcation problem in a fractional-order delayed food chain model that incorporates a fear effect. We observe that the fractional order significantly impacts the delayed system, influencing its stability in the presence of fear. Both the fractional order and the fear effect play crucial roles in determining the system’s stability. Furthermore, we observe stability switching induced by the fear effect while keeping the delay fixed. We identify the stability condition of the proposed model and precisely establish bifurcation points by utilizing delay as a bifurcation parameter. The system exhibits robust stability performance with smaller control parameters, and Hopf bifurcation arises as the control parameter surpasses a critical value. Additionally, through theoretical analysis and numerical simulations, we investigate the effects of fractional order, the fear effect, and time delay on the system’s stability.

In this paper, we investigates the sliding mode control (SMC) strategy for neutral-type systems with distributed time-varying delay using novel improved integral inequality, which has significant applications in fields such as control systems, communication networks, and biological systems. A standard Lyapunov-Krasovskii functional is introduced, complemented by improved integral inequality techniques and two types of time-delay methods (neutral type and distributed). These methodologies enabled the derivation of sufficient conditions, formulated as linear matrix inequalities, that ensured the asymptotic stability of the system utilizing the SMC technique. The proposed approach reduced conservatism in stability criteria by investigating improved integral inequalities and delay-dependent techniques, offering more accurate and efficient stability conditions. Numerical examples are presented to validate the theoretical findings with the practical application of partial element equivalent circuit (PEEC), showcasing the effectiveness and superiority of the proposed methodology over existing results.

This study investigates the global asymptotic stability of hybrid bidirectional associative memory (BAM) complex-valued neural networks (CVNNs) with time-varying delays and uncertain parameters, where the system matrices are assumed to be symmetric. By constructing an appropriate Lyapunov–Krasovskii functional (LKF), new sufficient conditions are derived to guarantee the existence and uniqueness of equilibrium points, as well as to establish the global asymptotic stability of the proposed symmetric hybrid BAM CVNNs. The validity and effectiveness of the theoretical results are further demonstrated through detailed numerical examples.