Sampling-Based Event-Triggered Exponential Synchronization for Reaction-Diffusion Neural Networks

Fuzzy spatiotemporal event-triggered control for the synchronization of IT2 T-S fuzzy CVRDNNs with mini-batch machine learning supervision

This paper is centered on the development of a fuzzy memory-based spatiotemporal event-triggered mechanism (FMSETM) for the synchronization of the drive-response interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy complex-valued reaction-diffusion neural networks (CVRDNNs). CVRDNNs have a higher processing capability and can perform better than multilayered real-valued RDNNs. Firstly, a general IT2 T-S fuzzy neural network model is constructed by considering complex-valued parameters and the reaction-diffusion terms. Secondly, a mini-batch semi-stochastic machine learning technique is proposed to optimize the maximum sampling period in an FMSETM. Furthermore, by constructing an asymmetric Lyapunov functional (LF) dependent on the membership function (MF), certain symmetric and positive-definite constraints of matrices are removed. The synchronization criteria are derived via linear matrix inequalities (LMIs) for the IT2 T-S fuzzy CVRDNNs. Finally, two numerical examples are utilized to corroborate the feasibility of the developed approach. From the simulation results, it can be seen that introducing machine learning techniques into the synchronization problem of CVRDNNs can improve the efficiency of convergence.

Adaptive Drive-Response Synchronization of Timescale-Type Neural Networks With Unbounded Time-Varying Delays

In recent years, adaptive drive-response synchronization (DRS) of two continuous-time delayed neural networks (NNs) has been investigated extensively. For two timescale-type NNs (TNNs), how to develop adaptive synchronization control schemes and demonstrate rigorously is still an open problem. This article concentrates on adaptive control design for synchronization of TNNs with unbounded time-varying delays. First, timescale-type Barbalat lemma and novel timescale-type inequality techniques are first proposed, which provides us practical methods to investigate timescale-type nonlinear systems. Second, using timescale-type calculus, novel timescale-type inequality, and timescale-type Barbalat lemma, we demonstrate that global asymptotic synchronization can be achieved via adaptive control under algebraic and matrix inequality criteria even if the time-varying delays are unbounded and nondifferentiable. Adaptive DRS is discussed for TNNs, which implies our control schemes are suitable for continuous-time NNs, their discrete-time counterparts, and any combination of them. Finally, numerical examples on TNNs and timescale-type chaotic Ikeda-like oscillator with unbounded time-varying delays are carried out to verify the adaptive control schemes.

Dynamic-Memory Protocol-Based Synchronization for Semi-Markov Jump Reaction-Diffusion CDNs

This study investigates the synchronization of reaction-diffusion complex dynamical networks (CDNs) based on semi-Markov switching topology and an event-triggered protocol. The investigated model is rendered more practical via the introduction of a semi-Markov process for stochastic jump CDNs. Based on the internal dynamic variable history information, a dynamic-memory event-triggered strategy is proposed, wherein the primary novelty lies in its prior transmitted packets to enhance the control performance. This further reduces data transmission based on the dynamic threshold parameters. The Bessel-Legendre inequality is adopted to reduce the conservatism of the obtained results. In addition, sufficient synchronization conditions are established to ensure the stochastic stability of the error system for two different models (partial differential equations-and ordinary differential equations-based models). Furthermore, two examples are provided to illustrate the effectiveness of the theoretical results.

Observer-Based Boundary Stabilization of Coupled Semilinear Reaction-Diffusion Neural Networks With Spatially Varying Coefficients via Event-Triggered Controller

The aim of this article is to propose an observer-based event-triggered Robin boundary control strategy for the exponential stabilization of the coupled semilinear reaction-diffusion neural networks with spatially varying coefficients. Toward this aim, we design an observer to estimate the value of system states by using some of these system values as the available measurement. An observer-based event-triggered boundary stabilizer is then presented to exponentially stabilize the considered systems with the Zeno behavior being excluded. Throughout this article, the main used method is backstepping, which yields an explicit expression of the control formulae. Moreover, we see that the proposed event-triggered boundary control scheme can ensure the desired level of control performance with fewer control law updates. A numerical example is finally given to illustrate the effectiveness of our proposed method.

Event-triggered distributed secure state estimation for homologous sensor attacks

Network security technology is capable of mitigating attack signals launched by malicious attackers, thereby safeguarding and bolstering system security. Secure state estimation is a type of network defense technology that involves reconstructing the system state by measuring potential attacks. The relevant research has also been expanded to distributed systems to enable collaborative secure state estimation among different agents facing homogeneous attacks; however, such research significantly enhance the communication load of the system. On the other hand, if the homogeneous attack is non-existent or consistent, excessive network communication is definitely inefficient. An event-triggered approach is a potent technique that can alleviate communication overhead while having minimal impact on system performance. This paper proposes a mechanism for event-triggered transmission in the context of distributed secure state estimation. While communication latency and data transmission disorder may be present within the communication network, the estimation error of the proposed observer is constrained and bounded. The simulation results indicate that for stable malicious attack signals, system communication can be divided into high-frequency and low-frequency communication periods. The simulation results also shows that the maximum estimation error is proportional to the trigger threshold, allowing the observer's performance to be finely tuned to the required precision.