Resilient asynchronous state estimation of Markov switching neural networks: A hierarchical structure approach

This paper deals with the issue of resilient asynchronous state estimation of discrete-time Markov switching neural networks. Randomly occurring signal quantization and packet dropout are involved in the imperfect measured output. The asynchronous switching phenomena appear among Markov switching neural networks, quantizer modes and filter modes, which are modeled by a hierarchical structure approach. By resorting to the hierarchical structure approach and Lyapunov functional technique, sufficient conditions are achieved, and asynchronous resilient filters are derived such that filtering error dynamic is stochastically stable. Finally, two examples are included to verify the validity of the proposed method. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

This paper addresses the resilient asynchronous state estimation of discrete-time Markov switching neural networks, deriving asynchronous resilient filters using a hierarchical structure approach and Lyapunov functional technique. The validity of the proposed method is verified through two examples.