Results concerning the absolute stability of delayed neural networks

Joy, M. (2000) Results concerning the absolute stability of delayed neural networks. Neural Networks, 13(6), pp. 613-616. ISSN (print) 0893-6080

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Abstract

We report on results concerning the global asymptotic stability (GAS) and absolute stability (ABST) of delay models of continuous-time neural networks. These results present sufficient conditions for GAS and in case the network has instantaneous signalling as well as delay signalling (for example, a delayed cellular neural network (DCNN)), are milder than previously known criteria; they apply to neural networks with a broad range of activation functions assuming neither differentiability nor strict monotonicity. We are therefore able to interpret the results as guarantees of absolute stability of the network with respect to the wide class of admissible activation functions. Furthermore, these results do not assume symmetry of the connection matrices. We also present a sufficient condition for absolute stability in the presence of nonconstant delays.

Item Type: Article
Uncontrolled Keywords: global asymptotic stability, absolute stability, delayed neural networks, lyapunov functional, lyapunov diagonal stability
Research Area: Biological sciences
Faculty, School or Research Centre: Faculty of Computing, Information Systems and Mathematics (until 2011)
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Depositing User: Automatic Import Agent
Date Deposited: 11 Mar 2010 16:36
Last Modified: 15 Jun 2011 13:33
URI: http://eprints.kingston.ac.uk/id/eprint/7236

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