New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural Networks | Zendy

Jianjiang Yu | Zendy

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New Results on Passivity Analysis of Delayed Discrete-Time Stochastic Neural Networks

Author(s) -

Jianjiang Yu

Publication year - 2009

Publication title -

discrete dynamics in nature and society

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.264

H-Index - 39

eISSN - 1607-887X

pISSN - 1026-0226

DOI - 10.1155/2009/139671

Subject(s) - passivity , interval (graph theory) , artificial neural network , discrete time and continuous time , generalization , stochastic neural network , stability (learning theory) , computer science , class (philosophy) , control theory (sociology) , mathematics , matrix (chemical analysis) , recurrent neural network , control (management) , artificial intelligence , statistics , mathematical analysis , machine learning , materials science , combinatorics , electrical engineering , composite material , engineering

The problem of passivity analysis for a class of discrete-time stochastic neural networks (DSNNs) with time-varying interval delay is investigated. The delay-dependent sufficient criteria are derived in terms of linear matrix inequalities (LMIs). The results are shown to be generalization of some previous results and are less conservative than the existing works. Meanwhile, the computational complexity of the obtained stability conditions is reduced because less variables are involved. Two numerical examples are given to show the effectiveness and the benefits of the proposed method

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