An LMI based chaotic passivity analysis on memristive neural networks for memductance function

Abstract This paper is devoted to passivity analysis for a class of chaotic memristive neural networks with distinct memductance approach, subject to actuator failures. Based on the existence of memristor, actuators and activation function, it was possible for the proposed model to stay in a stable state and reach the critical point by designing a strong reliable state-feedback controller. The qualitative analysis of this model can be developed using differential inclusion theory in the sense of Fillipov’s solution with suitable Lyapunov functional to acquire the results in terms of linear matrix inequalities (LMIs). Considering the known and unknown actuators cases, some sufficient conditions are derived for both state-dependent switched system and state-dependent continuous system based on passivity theory along with its chaotic phenomena. The reliable state-feedback controller is designed to guarantee that the considered closed loop system is internally stable by adopting the stabilizing control law. Finally, numerical examples are presented to demonstrate theoretical results via graphical illustrations.