Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses | Zendy

Kaihong Zhao | Zendy; Liwenjing Wang | Zendy; Juqing Liu | Zendy

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Global Robust Attractive and Invariant Sets of Fuzzy Neural Networks with Delays and Impulses

Author(s) -

Kaihong Zhao,

Liwenjing Wang,

Juqing Liu

Publication year - 2013

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2013/935491

Subject(s) - artificial neural network , invariant (physics) , mathematics , control theory (sociology) , computer science , fuzzy logic , differential (mechanical device) , artificial intelligence , control (management) , engineering , mathematical physics , aerospace engineering

A class of fuzzy neural networks (FNNs) with time-varying delays and impulses is investigated. With removing some restrictions on the amplification functions, a new differential inequality is established, which improves previouse criteria. Applying this differential inequality, a series of new and useful criteria are obtained to ensure the existence of global robust attracting and invariant sets for FNNs with time-varying delays and impulses. Our main results allow much broader application for fuzzy and impulsive neural networks with or without delays. An example is given to illustrate the effectiveness of our results

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