期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 27, 期 4, 页码 903-909出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2015.2425962
关键词
Fuzzy neural networks; Markovian jump parameters; mixed H-infinity and passivity performance
类别
资金
- 111 Project [B12018]
- National Natural Science Foundation of China [61174058]
- Australian Research Council [DP140102180, LP140100471]
- Natural Science Foundation of Henan Province, China [132300410013]
- Plan of Nature Science Fundamental Research in Henan University of Technology [2012JCYJ13]
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.
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