Finite-time [formula omitted] asynchronous state estimation for discrete-time fuzzy Markov jump neural networks with uncertain measurements

Abstract

This paper is concerned with the problem of the H∞ asynchronous state estimation for fuzzy Markov jump neural networks (FMJNNs) with uncertain measurements over a finite-time interval. In terms of a Bernoulli distributed white sequence, the phenomenon of the randomly occurring uncertainties in the output equation is represented by exploiting a random variable with known occurrence probabilities. The main focus of this paper is to present a state estimator such that the resulting error system is finite-time bounded and satisfies an H∞ performance requirement. Then, by employing the stochastic analysis technique, sufficient conditions are provided to ensure that the state estimator is designed by means of solving a convex optimization problem. An example is finally given to explain the effectiveness and potentiality of the proposed design method.