On global asymptotic stability of fully connected recurrent neural networks

Conditions for global asymptotic stability (GAS) of a nonlinear relaxation process realized by a recurrent neural network (RNN) are provided. Existence, convergence, and robustness of such a process are analyzed. This is undertaken based upon the contraction mapping theorem (CMT) and the correspondi...

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Bibliographic Details

Main Authors:	Danilo P. Mandic, Jonathon Chambers, Milorad M. Bozic
Format:	Default Conference proceeding
Published:	2000
Subjects:	Mechanical engineering not elsewhere classified untagged Mechanical Engineering not elsewhere classified
Online Access:	https://hdl.handle.net/2134/5794
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