Finite-Time Anti-synchronization Control of Memristive Neural Networks With Stochastic Perturbations

Abstract

In this paper, finite-time anti-synchronization control of memristive neural networks with stochastic perturbations is studied. We investigate a class of memristive neural networks with two different types of memductance functions. The purpose of the addressed problem is to design a nonlinear controller which can obtain anti-synchronization of the drive system and the response system in finite time. Based on two kinds of memductance functions, finite-time stability criteria are obtained for memristive neural networks with stochastic perturbations. The analysis in this paper employs differential inclusions theory, finite-time stability theorem, linear matrix inequalities and Lyapunov functional method. These theoretical analysis can characterize fundamental electrical properties of memristive systems and provide convenience for applications in pattern recognition, associative memories, associative learning, etc.. Finally, two numerical examples are given to show the effectiveness of our results.