Multiperiodicity analysis and numerical simulation of discrete-time transiently chaotic non-autonomous neural networks with time-varying delays

In this paper, we investigate multiperiodicity analysis of discrete-time transiently chaotic neural networks, i.e., the coexistence and exponential stability of multiple periodic sequence solutions. By using analytic property of activation functions and Schauder’s fixed point theorem, we attain the coexistence of 2N2N periodic sequence solutions. Meanwhile, some new and simple criteria are derived for the networks to converge exponentially toward 2N2N periodic sequence solutions. Our results are new and complement existing ones in the literature. Finally, computer numerical simulations are performed to illustrate multiperiodicity of discrete-time transiently chaotic neural networks.