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 employ...Show MoreMetadata
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 (U, L, V)-dissipative. Finally, the effectiveness and practicability of the theoretical results are verified by two examples, including a biological network.
Published in: IEEE Transactions on Systems, Man, and Cybernetics: Systems ( Volume: 49, Issue: 2, February 2019)