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https://hdl.handle.net/2440/113796
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Type: | Journal article |
Title: | Asynchronous filtering for Markov jump neural networks with quantized outputs |
Author: | Shen, Y. Wu, Z. Shi, P. Su, H. Huang, T. |
Citation: | IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018; 49(2):433-443 |
Publisher: | IEEE |
Issue Date: | 2018 |
ISSN: | 2168-2216 2168-2232 |
Statement of Responsibility: | 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 |
DOI: | 10.1109/TSMC.2017.2789180 |
Grant ID: | http://purl.org/au-research/grants/arc/DP170102644 |
Published version: | http://dx.doi.org/10.1109/tsmc.2017.2789180 |
Appears in Collections: | Aurora harvest 3 Electrical and Electronic Engineering publications |
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