Mixed H∞ and passivity based state estimation for fuzzy neural networks with Markovian-type estimator gain change

Abstract

This paper is concerned with the mixed H∞ and passivity based state estimation for a class of discrete-time fuzzy neural networks with the estimator gain change, where a discrete-time homogeneous Markov chain taking value in a finite set Γ={0,1} is introduced to model this phenomenon. Based on the Markovian system approach and linear matrix inequality technique, a new sufficient condition has been derived such that the estimation error system is exponentially stable in the mean square sense and achieves a prescribed mixed H∞ and passivity performance level. The estimator parameter is then determined by solving a set of linear matrix inequalities (LMIs). A numerical example is presented to show the effectiveness of the proposed design method.