Asynchronous Filtering for Markov Jump Neural Networks With Quantized Outputs

Abstract

In this paper, an asynchronous filter is proposed for Markov jump neural networks (NNs) with time delay and quantized measurements where a logarithmic quantizer is employed. The filter and quantizer are both mode-dependent and their modes are asynchronous with that of the NN, which is described by hidden Markov models. By the Lyapunov–Krasovskii functional approach, a sufficient condition is derived and a filter is then designed such that the filtering error dynamics are stochastically mean square stable and strictly $\\boldsymbol {(\\mathscr U,\\mathscr S,\\mathscr V)}$ -dissipative. Finally, the effectiveness and practicability of the theoretical results are verified by two examples, including a biological network.