New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay | Zendy

Zixin Liu | Zendy; Shu Lv | Zendy; Shouming Zhong | Zendy; Mao Ye | Zendy

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New Improved Exponential Stability Criteria for Discrete-Time Neural Networks with Time-Varying Delay

Author(s) -

Zixin Liu,

Shu Lv,

Shouming Zhong,

Mao Ye

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 - 10.1155/2009/874582

Subject(s) - discrete time and continuous time , mathematics , stability (learning theory) , artificial neural network , control theory (sociology) , connection (principal bundle) , exponential stability , computer science , nonlinear system , statistics , artificial intelligence , machine learning , physics , geometry , control (management) , quantum mechanics

The robust stability of uncertain discrete-time recurrent neural networks with time-varying delay is investigated. By decomposing some connection weight matrices, new Lyapunov-Krasovskii functionals are constructed, and serial new improved stability criteria are derived. These criteria are formulated in the forms of linear matrix inequalities (LMIs). Compared with some previous results, the new results are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method

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