Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/117101
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DC Field | Value | Language |
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dc.contributor.author | Lu, R. | - |
dc.contributor.author | Tao, J. | - |
dc.contributor.author | Shi, P. | - |
dc.contributor.author | Su, H. | - |
dc.contributor.author | Wu, Z. | - |
dc.contributor.author | Xu, Y. | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | IEEE Transactions on Neural Networks and Learning Systems, 2018; 29(5):1888-1899 | - |
dc.identifier.issn | 2162-237X | - |
dc.identifier.issn | 2162-2388 | - |
dc.identifier.uri | http://hdl.handle.net/2440/117101 | - |
dc.description.abstract | 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. | - |
dc.description.statementofresponsibility | Renquan Lu, Jie Tao, Peng Shi, Hongye Su, Zheng-Guang Wu, and Yong Xu | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.rights | © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. | - |
dc.source.uri | http://dx.doi.org/10.1109/tnnls.2017.2688582 | - |
dc.subject | Dissipativity | - |
dc.subject | Neural networks | - |
dc.subject | Periodic Markov jump systems | - |
dc.subject | Quantization | - |
dc.subject | Resilient filter | - |
dc.title | Dissipativity-based resilient filtering of periodic markovian jump neural networks with quantized measurements | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1109/TNNLS.2017.2688582 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP170102644 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Shi, P. [0000-0001-8218-586X] | - |
Appears in Collections: | Aurora harvest 3 Electrical and Electronic Engineering publications |
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