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|Title:||Mixed H-infinity and passive filtering for discrete fuzzy neural networks with stochastic jumps and time delays|
|Citation:||IEEE Transactions on Neural Networks and Learning Systems, 2016; 27(4):903-909|
|Publisher:||Institute of Electrical and Electronics Engineers|
|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.|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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