Asynchronous filtering for Markov jump neural networks with quantized outputs
Date
2018
Authors
Shen, Y.
Wu, Z.
Shi, P.
Su, H.
Huang, T.
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Journal article
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IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018; 49(2):433-443
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Ying Shen, Zheng-Guang Wu, Peng Shi, Hongye Su, and Tingwen Huang
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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 ,S, V )-dissipative. Finally, the effectiveness and practicability of the theoretical results are verified by two examples, including a biological network.
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© 2018 IEEE