Exponential state estimation for Markovian jumping neural networks with time-varying discrete and distributed delays

Abstract: This paper is concerned with the exponential state estimation for Markovian jumping neural networks with time-varying discrete and distributed delays. The parameters of the neural networks are subject to the switching from one mode to another according to a Markov chain. By constructing a novel Lyapunov–Krasovskii functional and developing a new convex combination technique, a new delay-dependent exponential stability condition is proposed, such that for all admissible delay bounds, the resulting estimation error system is mean-square exponentially stable with a prescribed noise attenuation level in the sense. It is also shown that the design of the desired state estimator is achieved by solving a set of linear matrix inequalities (LMIs). The obtained condition implicitly establishes the relations among the maximum delay bounds, noise attenuation level and the exponential decay rate of the estimation error system. Finally, a numerical example is given to show the effectiveness of the proposed result. [Copyright &y& Elsevier]