Passivity Analysis of Stochastic Memristor-Based Complex-Valued Recurrent Neural Networks with Mixed Time-Varying Delays

Abstract

In this paper, the passivity analysis of stochastic memristor-based complex-valued recurrent neural networks (SMCVRNNs) with discrete and distributed time-varying delays is conducted. We adopt a switched system to describe the SMCVRNN with mixed time-varying delays. Appropriate Lyapunov–Krasovski functionals are constructed to analyze the passivity of SMCVRNNs under consideration. Two sufficient conditions are presented in terms of linear matrix inequalities which assure that the SMCVRNNs are stochastically passive. The effectiveness of the obtained results is demonstrated by two examples.