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Asynchronous Filtering for Markov Jump Neural Networks With Quantized Outputs | IEEE Journals & Magazine | IEEE Xplore

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 employ...Show More

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)
Page(s): 433 - 443
Date of Publication: 17 January 2018

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