Exponential Lag Synchronization of Memristive Neural Networks with Reaction Diffusion Terms via Neural Activation Function Control and Fuzzy Model

Abstract

This paper is concerned with the problem of exponential lag synchronization of memristive neural networks with reaction diffusion terms via neural activation function control and fuzzy model. An memristor-based circuit which exhibits the feature of pinched hysteresis is introduced and further, the memristive neural networks with reaction diffusion terms and such system containing fuzzy model are described at length, respectively. By utilizing the Lyapunov functional method and the neural activation function controller depending on the output of the system in the case of packed circuits, some concise conditions are acquired to guarantee the slave systems exponential lag synchronized with the master systems. Finally, several simulated examples are also presented to demonstrate the correctness of the theoretical results.

Distributed Event-Triggered Output Synchronization of Complex-Valued Memristive Reaction-Diffusion Complex Networks with Spatial Sampled-Data

“…Theorem 1. Under Assumptions 1, 2, and 3, using the distributed piecewise event-triggered mechanism (equation (23)) and the sampling controller (equation ( 21)), the master-slave networks systems (equations ( 1) and ( 3)) can be output quasisynchronized within the error Θ, and the error system (equation (18)) converges exponentially to the error level Θ with the convergence index κ 2 > 0, if the criteria are satisfied:…”

“…Until now, many worthwhile results about MRDNNs have been achieved. Global synchronization [21,22], exponential Lag synchronization [23], and fixed-time synchronization [24] of MRDNNs have been studied. Nonetheless, the mismatch of the master-slave system due to state-dependent parameters inevitably causes the synchronization error of the collective behaviour to change in a certain range.…”

Fixed‐time synchronization of neural networks with time delay via quantized intermittent control

“…Neural network is a kind of special complex network, which can be used to describe many familiar systems, such as associative memory, neural learning, and artificial NNs [1,2]. With the continuous in-depth study of NNs knowledge, the synchronization of NNs has gradually come into the public view [3][4][5].…”