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Global exponential stability analysis for a class of neural networks with time delays
Author(s) -
Guo Yingxin,
Liu Shu Tang
Publication year - 2011
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1761
Subject(s) - class (philosophy) , exponential stability , artificial neural network , stability (learning theory) , control theory (sociology) , fixed point theorem , computer science , mathematics , exponential function , point (geometry) , discrete mathematics , control (management) , nonlinear system , artificial intelligence , mathematical analysis , machine learning , physics , quantum mechanics , geometry
SUMMARY Employing Brouwer's fixed point theorem, matrix theory, we made a further investigation of a class of neural networks with delays in this paper. A family of sufficient conditions were given for checking global exponential stability. These results have important leading significance in the design and applications of globally stable neural networks with delays. Our results extended and improved some earlier publications. Copyright © 2011 John Wiley & Sons, Ltd.