State estimation of recurrent neural networks with two Markovian jumping parameters and mixed delays

This paper examines the problem of state estimation of recurrent neural networks with two Markovian jumping parameters and mixed delays. Based on the method of matrix decomposition and the technique of inequalities, several sufficient criteria are established in terms of linear matrix inequalities (LMIs). Compared with the existing results, the obtained conditions are more effective due to constructing a newly augmented Lyapunov-Krasovskii functional, which makes full use of the cross terms information. Numerical simulations are given to illustrate the effectiveness and advantage of the proposed method.