Journal
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 49, Issue 2, Pages 433-443Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2017.2789180
Keywords
Asynchronous filter; asynchronous quantization; dissipativity; hidden Markov model; Markov jump neural networks (MJNNs)
Funding
- Science Fund for Creative Research Groups of the National Natural Science Foundation of China [61621002]
- National Natural Science Foundation of China [61673339, 61773131, U1509217, 61573136, 61603133]
- Australian Research Council [DP170102644]
- Zhejiang Provincial Natural Science Foundation of China [LR16F030001]
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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 arc 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, g, v)-dissipative. Finally, the effectiveness and practicability of the theoretical results are verified by two examples, including a biological network.
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