Non-fragile state observer design for neural networks with Markovian jumping parameters and time-delays

This paper investigates the non-fragile observer based design for neural networks with mixed time-varying delays and Markovian jumping parameters. By developing a reciprocal convex approach and based on the Lyapunov–Krasovskii functional, and stochastic stability theory, a delay-dependent stability criterion is obtained in terms of linear matrix inequalities (LMIs). The observer gains are given from the LMI feasible solutions. Finally, three numerical examples are given to illustrate the effectiveness of the derived theoretical results. Among them the third example deals the practical system of quadruple tank process.