Adaptive Event-Triggered Lag Outer Synchronization for Coupled Neural Networks With Multistate or Multiderivative Couplings.

Multistate coupled coupled neural networks (MSCCNN) and multiderivative coupled coupled neural networks (MDCCNN) are introduced in this article, and the lag outer synchronization for these two networks are tackled. First, a lag outer synchronization criterion for MSCCNN is derived using a node-based adaptive event-triggered control scheme, and the fact that the Zeno behavior does not exist is also proved. Moreover, the edge-based adaptive event-triggered control method is also utilized to address the lag outer synchronization for MSCCNN, and the existence of Zeno behavior is ruled out.

Finite-Time Synchronization and H Synchronization for Coupled Neural Networks With Multistate or Multiderivative Couplings.

This article investigates the finite-time synchronization (FTS) and H synchronization for two types of coupled neural networks (CNNs), that is, the cases with multistate couplings and with multiderivative couplings. By designing appropriate state feedback controllers and parameter adjustment strategies, some FTS and finite-time H synchronization criteria for CNNs with multistate couplings are derived. In addition, we further consider the FTS and finite-time H synchronization problems for CNNs with multiderivative couplings by utilizing state feedback control approach and selecting suitable parameter adjustment schemes.

Analysis and Control of Output Synchronization in Directed and Undirected Complex Dynamical Networks.

This research focuses on the problem of output synchronization in undirected and directed complex dynamical networks, respectively, by applying Barbalat's lemma. First, to ensure the output synchronization, several sufficient criteria are established for these network models based on some mathematical techniques, such as the Lyapunov functional method and matrix theory. Furthermore, some adaptive schemes to adjust the coupling weights among network nodes are developed to achieve the output synchronization.

Passivity and Output Synchronization of Complex Dynamical Networks With Fixed and Adaptive Coupling Strength.

This paper considers a complex dynamical network model, in which the input and output vectors have different dimensions. We, respectively, investigate the passivity and the relationship between output strict passivity and output synchronization of the complex dynamical network with fixed and adaptive coupling strength. First, two new passivity definitions are proposed, which generalize some existing concepts of passivity.

Passivity of Directed and Undirected Complex Dynamical Networks With Adaptive Coupling Weights.

A complex dynamical network consisting of N identical neural networks with reaction-diffusion terms is considered in this paper. First, several passivity definitions for the systems with different dimensions of input and output are given. By utilizing some inequality techniques, several criteria are presented, ensuring the passivity of the complex dynamical network under the designed adaptive law.

Passivity and Synchronization of Linearly Coupled Reaction-Diffusion Neural Networks With Adaptive Coupling.

In this paper, we study a general array model of coupled reaction-diffusion neural networks (NNs) with adaptive coupling. In order to ensure the passivity of the coupled reaction-diffusion neural networks, some adaptive strategies to tune the coupling strengths among network nodes are designed. By utilizing some inequality techniques and the designed adaptive laws, several sufficient conditions ensuring passivity are obtained.

Pinning Control Strategies for Synchronization of Linearly Coupled Neural Networks With Reaction-Diffusion Terms.

Two types of coupled neural networks with reaction-diffusion terms are considered in this paper. In the first one, the nodes are coupled through their states. In the second one, the nodes are coupled through the spatial diffusion terms.