Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/101065
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Type: Journal article
Title: Mixed H-infinity and passive filtering for discrete fuzzy neural networks with stochastic jumps and time delays
Author: Shi, P.
Zhang, Y.
Chadli, M.
Agarwal, R.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2016; 27(4):903-909
Publisher: Institute of Electrical and Electronics Engineers
Issue Date: 2016
ISSN: 2162-237X
2162-2388
Statement of
Responsibility: 
Peng Shi, Yingqi Zhang, Mohammed Chadli, and Ramesh K. Agarwal
Abstract: In this brief, the problems of the mixed H-infinity and passivity performance analysis and design are investigated for discrete time-delay neural networks with Markovian jump parameters represented by Takagi–Sugeno fuzzy model. The main purpose of this brief is to design a filter to guarantee that the augmented Markovian jump fuzzy neural networks are stable in mean-square sense and satisfy a prescribed passivity performance index by employing the Lyapunov method and the stochastic analysis technique. Applying the matrix decomposition techniques, sufficient conditions are provided for the solvability of the problems, which can be formulated in terms of linear matrix inequalities. A numerical example is also presented to illustrate the effectiveness of the proposed techniques.
Keywords: Fuzzy neural networks; Biological neural networks; Neurons; Performance analysis; Stability analysis; Symmetric matrices; Learning systems
Rights: © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
RMID: 0030046968
DOI: 10.1109/TNNLS.2015.2425962
Grant ID: http://purl.org/au-research/grants/arc/DP140102180
http://purl.org/au-research/grants/arc/LP140100471
Appears in Collections:Electrical and Electronic Engineering publications

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