NDLI: New global asymptotic stability criteria of Hopfield neural network with time-varying delays

Content Provider	IEEE Xplore Digital Library
Author	Zhengxia Wang Degang Yang Fengqing Han Xinyuan Liang
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Chongqing Univ., Chongqing (Zhengxia Wang)
Abstract	In this paper, the global stability of Hopfield neural networks with time-varying delays is investigated by utilizing Lyapunov functional method and the linear matrix inequality (LMI) technique. Distinct difference from other analytical approach lies in ldquolinearizationrdquo of neural network model, by which the considered neural network model is transformed into a linear time-variant system. Then, a process, which is called parameterized first-order model transformation, is used to transform the linear process. Novel criteria for global asymptotic stability of the unique equilibrium point of Hopfield neural network with time-varying delays are obtained. the obtained results are less conservative and restrictive than those established in the earlier references. Numerical examples are given to show the effectiveness of our proposed method.
Starting Page	7125
Ending Page	7129
File Size	138870
Page Count	5
File Format	PDF
ISBN	9781424421138
DOI	10.1109/WCICA.2008.4594023
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-25
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Delay Asymptotic stability Stability analysis Linear matrix inequalities Stability criteria Biological system modeling Lyapunov method Global asymptotic stability Delayed Hopfield neural networks Linear matrix inequality
Content Type	Text
Resource Type	Article

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