Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26530
Title: Absolute exponential stability of a class of recurrent neural networks with multiple and variable delays
Authors: Lu, H
Shen, R
Chung, FL 
Keywords: Absolute exponential stability
Delays
Lyapunov functionals
Recurrent neural networks
Issue Date: 2005
Publisher: Elsevier
Source: Theoretical computer science, 2005, v. 344, no. 2-3, p. 103-119 How to cite?
Journal: Theoretical computer science 
Abstract: In this paper, we derive some new conditions for absolute exponential stability (AEST) of a class of recurrent neural networks with multiple and variable delays. By using the Holder's inequality and the Young's inequality to estimate the derivatives of the Lyapunov functionals, we are able to establish more general results than some existing ones. The first type of conditions established involves the convex combinations of column-sum and row-sum dominance of the neural network weight matrices, while the second type involves the p-norm of the weight matrices with p∈[1,+∞].
URI: http://hdl.handle.net/10397/26530
ISSN: 0304-3975
DOI: 10.1016/j.tcs.2005.02.006
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