Non-fragile mixed H∞ and passive asynchronous state estimation for Markov jump neural networks with randomly occurring uncertainties and sensor nonlinearity

This paper is concerned with the non-fragile mixed H∞ and passive asynchronous state estimation problem for uncertain discrete-time Markov jump neural networks (MJNNs). Both the uncertainties of system and the sensor nonlinearity are considered to be randomly occurring which are governed by a set of Bernoulli distributed white sequences. Since inaccuracies or uncertainties may occur in the designed state estimator and the complete mode synchronization between plant and state estimator is hardly possible, a non-fragile asynchronous state estimator design method is presented. By using an optimize matrix decoupling approach and Lyapunov-Krasovskii methodology, some sufficient conditions for the existence of non-fragile mixed H∞ and passive asynchronous state estimator are proposed. A numerical example is presented to demonstrate the effectiveness of our proposed method.