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Neural Processing Letters

01.10.2016

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

verfasst von: Weiqiang Gong, Jinling Liang, Congjun Zhang, Jinde Cao

Erschienen in: Neural Processing Letters | Ausgabe 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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Metadaten
Titel
Nonlinear Measure Approach for the Stability Analysis of Complex-Valued Neural Networks
verfasst von
Weiqiang Gong
Jinling Liang
Congjun Zhang
Jinde Cao
Publikationsdatum
01.10.2016
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2016
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-015-9475-9

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