Abstract
This paper investigates the synchronization control issue for a set of switched inertial neural networks in the discrete-time domain, in which the persistent dwell-time switching law is employed to depict the switchings among system parameters. Thereinto, the foregoing networks having the second-order differential equations are degraded to the first-order differential ones through applying the variable transformation. In addition, in order to cope with random gain fluctuations caused by noise or harsh environments, in the controller, two random variables obeying the Bernoulli distribution are employed to simulate the occurrence of gain fluctuations. Based on Lyapunov stability theory, persistent dwell-time concept and stochastic analysis theory, some sufficient criteria are derived under which the synchronization error system is exponentially mean-square stable with a prescribed l2-l∞ property. Finally, a numerical example, including some illustrative simulations, is given to present the feasibility of the derived analytical results.
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