Non-fragile synchronization of memristive BAM networks with random feedback gain fluctuations

Abstract

This paper investigates the non-fragile synchronization for bidirectional associative memory (BAM) neural networks with time-varying delays. A memristor-based BAM neural network is formulated and sufficient conditions are derived to guarantee its synchronization based on master–slave system approach. The non-fragile observer based feedback controller gains are assumed to have the random fluctuations, two different types of uncertainties which perturb the gains are taken into account. The Lyapunov–Krasovskii stability theory together with linear matrix inequality (LMI) approach is used to derive the delay-dependent criteria to ensure the asymptotic stability of the error system, which guarantees the master system synchronize with the slave system. The expressions for the non-fragile controller can be obtained by solving a set of LMIs using standard MATLAB toolboxes. Finally, numerical example on the chaotic system is presented to illustrate the effectiveness of the theoretical results.