State estimation of discrete-time markov jump neural networks with general transition probabilities and output quantization

Abstract

This paper concerns the problem of state estimation of discrete-time Markov jump neural networks with general transition probabilities and output quantization. In terms of a Markov chain, the event of mode switching at various times is considered in both the parameters and the discrete delays of the neural networks. The state estimation is analyzed when the information is transmitted over a digital communication channel. In this concern the design of the quantizer and the estimator is jointly investigated. The purpose of the concerned problem is to design a mode-dependent state estimator such that the network states are estimated through available output measurements such that the dynamics of the estimation error is stochastically stable. Novel Lyapunov–Krasovskii functional is constructed and sufficient constraints are derived in terms of linear matrix inequalities such that the existence of the desired estimator is assured. The effectiveness of the proposed approach is illustrated through a simulation example.