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.
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