Exponential stability of neural networks with asymmetric connection weights

Jinxiang Yang and Shouming Zhong

Chaos, Solitons & Fractals, 2007, vol. 34, issue 2, 580-587

Abstract: This paper investigates the exponential stability of a class of neural networks with asymmetric connection weights. By dividing the network state variables into various parts according to the characters of the neural networks, some new sufficient conditions of exponential stability are derived via constructing a Lyapunov function and using the method of the variation of constant. The new conditions are associated with the initial values and are described by some blocks of the interconnection matrix, and do not depend on other blocks. Examples are given to further illustrate the theory.

Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:34:y:2007:i:2:p:580-587

DOI: 10.1016/j.chaos.2006.03.076

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