Convergence analysis of general neural networks under almost periodic stimuli

Authors:
Zhenkun Huang;S. Mohamad;Xinghua Wang;Cg1 hunhua Feng
Affiliations:
School of Sciences, Jimei University, Xiamen 361021, People's Republic of China and Department of Mathematics, Faculty of Science, Universiti Brunei Darussalam, Gadong BE 1410, Brunei Darussalam;Department of Mathematics, Faculty of Science, Universiti Brunei Darussalam, Gadong BE 1410, Brunei Darussalam;Department of Mathematics, Zhejiang University, Hangzhou 310027, People's Republic of China;College of Mathematical Science, Guangxi Normal University, Guilin 541004, People's Republic of China
Venue:
International Journal of Circuit Theory and Applications
Year:
2009

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Abstract

In this paper, we investigate the convergence dynamics of 2N almost periodic encoded patterns of general neural networks subjected to external almost periodic stimuli, including almost periodic delays. Invariant regions are established for the existence of 2N almost periodic encoded patterns under two classes of activation functions. By employing the property of ℳ-cone and inequality technique, attracting basins are estimated and some criteria are derived for the networks to converge exponentially toward 2N almost periodic encoded patterns. The results obtained are new; they extend and generalize the corresponding results existing in the previous literature. Copyright © 2008 John Wiley & Sons, Ltd.