Extended Dissipative State Estimation for Markov Jump Neural Networks With Unreliable Links

TLDR

Extended dissipativity-based state estimation for discrete‑time Markov jump neural networks with time‑varying transition probabilities and unreliable communication links subject to quantization and packet dropouts is the focus of this study. The paper proposes a Markov switching estimator that guarantees extended stochastic dissipativity of the error system under simultaneous packet dropouts and signal quantization. The design method derives sufficient conditions and constructs an explicit Markov switching estimator satisfying the dissipativity requirements. The derived conditions are shown to be solvable, the estimator is explicitly expressed, and two examples demonstrate the method’s effectiveness.

Abstract

This paper is concerned with the problem of extended dissipativity-based state estimation for discrete-time Markov jump neural networks (NNs), where the variation of the piecewise time-varying transition probabilities of Markov chain is subject to a set of switching signals satisfying an average dwell-time property. The communication links between the NNs and the estimator are assumed to be imperfect, where the phenomena of signal quantization and data packet dropouts occur simultaneously. The aim of this paper is to contribute with a Markov switching estimator design method, which ensures that the resulting error system is extended stochastically dissipative, in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links. Sufficient conditions for the solvability of such a problem are established. Based on the derived conditions, an explicit expression of the desired Markov switching estimator is presented. Finally, two illustrated examples are given to show the effectiveness of the proposed design method.