Current studentsExisting staffContact us
 

Asynchronous filtering for Markov jump neural networks with quantized outputs

Simple item page
dc.contributor.authorShen, Y.
dc.contributor.authorWu, Z.
dc.contributor.authorShi, P.
dc.contributor.authorSu, H.
dc.contributor.authorHuang, T.
dc.date.issued2018
dc.description.abstractIn 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.
dc.description.statementofresponsibilityYing Shen, Zheng-Guang Wu, Peng Shi, Hongye Su, and Tingwen Huang
dc.identifier.citationIEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018; 49(2):433-443
dc.identifier.doi10.1109/TSMC.2017.2789180
dc.identifier.issn2168-2216
dc.identifier.issn2168-2232
dc.identifier.orcidShi, P. [0000-0001-6295-0405] [0000-0001-8218-586X] [0000-0002-0864-552X] [0000-0002-1358-2367] [0000-0002-5312-5435]
dc.identifier.urihttp://hdl.handle.net/2440/113796
dc.language.isoen
dc.publisherIEEE
dc.relation.granthttp://purl.org/au-research/grants/arc/DP170102644
dc.rights© 2018 IEEE
dc.source.urihttps://doi.org/10.1109/tsmc.2017.2789180
dc.subjectAsynchronous filter; asynchronous quantization; dissipativity; hidden Markov model; Markov jump neural networks (MJNNs)
dc.titleAsynchronous filtering for Markov jump neural networks with quantized outputs
dc.typeJournal article
pubs.publication-statusPublished

Files

Collections

Electrical and Electronic Engineering publications