Observer design for neutral-type neural networks with discrete and distributed time-varying delays

This paper is concerned with the problem of state estimation for a class of neural networks with discrete and distributed interval time-varying delays. We propose a new approach of nonlinear estimator design for the class of neutral-type neural networks. By constructing a newly augmented Lyapunov-Krasovskii functional, we establish sufficient conditions to guarantee the estimation error dynamics to be globally exponentially stable. The obtained results are formulated in terms of linear matrix inequalities (LMIs), which can be easily verified by the MATLAB LMI control toolbox. Then, the desired estimators gain matrix is characterized in terms of the solution to these LMIs. Three numerical examples are given to show the effectiveness of the proposed design method.