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|Title:||Asynchronous filtering for Markov jump neural networks with quantized outputs|
|Citation:||IEEE Transactions on Systems Man and Cybernetics: Systems, 2018; 49(2):433-443|
|Ying Shen, Zheng-Guang Wu, Peng Shi, Hongye Su, and Tingwen Huang|
|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.|
|Keywords:||Asynchronous filter; asynchronous quantization; dissipativity; hidden Markov model; Markov jump neural networks (MJNNs)|
|Rights:||© 2018 IEEE|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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