Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/117101
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Journal article |
Title: | Dissipativity-based resilient filtering of periodic markovian jump neural networks with quantized measurements |
Author: | Lu, R. Tao, J. Shi, P. Su, H. Wu, Z. Xu, Y. |
Citation: | IEEE Transactions on Neural Networks and Learning Systems, 2018; 29(5):1888-1899 |
Publisher: | IEEE |
Issue Date: | 2018 |
ISSN: | 2162-237X 2162-2388 |
Statement of Responsibility: | Renquan Lu, Jie Tao, Peng Shi, Hongye Su, Zheng-Guang Wu, and Yong Xu |
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. |
Keywords: | Dissipativity Neural networks Periodic Markov jump systems Quantization Resilient filter |
Rights: | © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. |
DOI: | 10.1109/TNNLS.2017.2688582 |
Grant ID: | http://purl.org/au-research/grants/arc/DP170102644 |
Published version: | http://dx.doi.org/10.1109/tnnls.2017.2688582 |
Appears in Collections: | Aurora harvest 3 Electrical and Electronic Engineering publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.