Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 29, Issue 5, Pages 1888-1899Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2017.2688582
Keywords
Dissipativity; neural networks; periodic Markov jump systems; quantization; resilient filter
Categories
Funding
- Australian Research Council [DP170102644]
- China National Funds for Distinguished Young Scientists [61425009]
- Zhejiang Provincial Natural Science Foundation of China [LR16F030001]
- Guangdong Province Higher Vocational Colleges and Schools Pearl River Scholar
- National Natural Science Foundation of China [61673339, 61573112, 61503106, U1509217]
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The problem of dissipativity-based resilient filtering for discrete-time periodic Markov jump neural networks in the presence of quantized measurements is investigated in this paper. Due to the limited capacities of network medium, a logarithmic quantizer is applied to the underlying systems. Considering the fact that the filter is realized through a network, randomly occurring parameter uncertainties of the filter are modeled by two mode-dependent Bernoulli processes. By establishing the mode-dependent periodic Lyapunov function, sufficient conditions are given to ensure the stability and dissipativity of the filtering error system. The filter parameters are derived via solving a set of linear matrix inequalities. The merits and validity of the proposed design techniques are verified by a simulation example.
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