This paper focuses on the hybrid effects of parameter uncertainty, stochastic perturbation, and impulses on global stability of delayed neural networks. By using the Ito formula, Lyapunov function, and Halanay inequality, we established several mean-square stability criteria from which we can estimate the feasible bounds of impulses, provided that parameter uncertainty and stochastic perturbations are well-constrained. Moreover, the present method can also be applied to general differential systems with stochastic perturbation and impulses.

This brief studies the exponential stabilization problem for a class of nonlinear systems by means of periodically intermittent control. An exponential stability criterion given in terms of a set of linear matrix inequalities, together with a simpler sufficient condition determined by three scalar inequalities, is presented. A suboptimal intermittent controller is designed with respect to the general cost function under the assumption that the control period and control width are fixed and known. Numerical simulations are presented to verify the theoretical results.

The problem of asynchronous dissipative control is investigated for Takagi-Sugeno fuzzy systems with Markov jump in this paper. Hidden Markov model is introduced to represent the nonsynchronization between the designed controller and the original system. By the fuzzy-basis-dependent and modedependent Lyapunov function, a sufficient condition is achieved such that the resulting closed-loop system is stochastically stable with a strictly (Q, S, R)-α-dissipative performance. The controller parameter is derived by applying MATLAB to solve a set of linear matrix inequalities. Finally, we present two examples to confirm the validity and correctness of our developed approach.