This paper presents some new results for the absolute stability of Hopfield neural networks with activation functions chosen from sigmoidal functions which have unbounded derivatives. Detailed discussions are also given to the relation and difference of absolute stabilities between neural networks and Lurie systems with multiple nonlinear controls. Although the basic idea of the absolute stability of neural networks comes from that of Lurie control systems, it provides a very useful practical model for the study of Lurie control Systems. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Liao, X., Xu, F., & Yu, P. (2006). Absolute stability of Hopfield neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3971 LNCS, pp. 249–254). Springer Verlag. https://doi.org/10.1007/11759966_38
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