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

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.