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Neural Processing Letters
Published in: Neural Processing Letters 2/2016

01-10-2016

Nonlinear Measure Approach for the Stability Analysis of Complex-Valued Neural Networks

Authors: Weiqiang Gong, Jinling Liang, Congjun Zhang, Jinde Cao

Published in: Neural Processing Letters | Issue 2/2016

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Abstract

Based on the nonlinear measure method and the matrix inequality techniques, this paper addresses the global asymptotic stability for the complex-valued neural networks with delay. Furthermore, robust stability of the addressed neural network with norm-bounded parameter uncertainties is also tackled. By constructing an appropriate Lyapunov functional candidate, several sufficient criteria are obtained to ascertain the existence, uniqueness and global stability of the equilibrium point of the addressed complex-valued neural networks, which are easy to be verified and implemented in practice. Finally, one example is given to illustrate the effectiveness of the obtained results.
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Metadata
Title
Nonlinear Measure Approach for the Stability Analysis of Complex-Valued Neural Networks
Authors
Weiqiang Gong
Jinling Liang
Congjun Zhang
Jinde Cao
Publication date
01-10-2016
Publisher
Springer US
Published in
Neural Processing Letters / Issue 2/2016
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-015-9475-9

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