Chaotic stabilization analysis for neutral-type memristive neural networks via reliable and sampled-data controller

This paper is concerned with chaotic stabilization analysis for a class of memristive neural networks (MNNs) with the effects of neutral delay and time-varying delay. We introduce a time-varying delay into reliable controller to enhance the model, while actuators experience failure signals. Sampled-data controller and reliable time-varying controller are implemented using input-delay approach and linear matrix inequality (LMI) approach. The main analysis of this present work is to utilize Lyapunov stability theory, differential inclusion theory and set-valued maps. By constructing a proper Lyapunov functionals, some sets of sufficient conditions are achieved in terms of LMIs. The considered MNNs with actuator fault guarantee the global asymptotic stability for known and unknown actuators cases. The process of congruence transformation has been applied to reliable time-varying controller to get a gain matrix. Finally, numerical examples demonstrate the less conservatism and effectiveness of the proposed results via MATLAB simulations.