research-article

Global robust stability analysis of uncertain neural networks with time varying delays

Authors:

Eylem YucelAuthors Info & Claims

Neurocomputing, Volume 167, Issue C

Pages 371 - 377

https://doi.org/10.1016/j.neucom.2015.04.058

Published: 01 November 2015 Publication History

Abstract

This paper deals with the global robust stability analysis of dynamical neural networks with time varying delays. By combining Lyapunov stability theorems and Homeomorphic mapping theorem, we obtain some original sufficient conditions for the existence, uniqueness and global asymptotic stability of the equilibrium point with respect to Lipschitz activation functions and under parameter uncertainties of the neural system. We also prove that the obtained robust stability conditions generalize some of the previously published corresponding literature results. The conditions we present can be easily verified as the conditions that are expressed in terms of the network parameters. Some comparative numerical examples are presented to demonstrate the advantages of our conditions over the previously published robust stability results.

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Cited By

Song QYu QZhao ZLiu YAlsaadi F(2018)Dynamics of complex-valued neural networks with variable coefficients and proportional delaysNeurocomputing10.1016/j.neucom.2017.11.041275:C(2762-2768)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.11.041
Hou NDong HWang ZRen WAlsaadi F(2017)H state estimation for discrete-time neural networks with distributed delays and randomly occurring uncertainties through Fading channelsNeural Networks10.1016/j.neunet.2016.12.00489:C(61-73)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1016/j.neunet.2016.12.004
Yu YDong HWang ZRen WAlsaadi F(2016)Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertaintiesNeurocomputing10.1016/j.neucom.2015.11.079182:C(18-24)Online publication date: 19-Mar-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.11.079

Index Terms

Global robust stability analysis of uncertain neural networks with time varying delays
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Neurocomputing

Neurocomputing Volume 167, Issue C

November 2015

688 pages

ISSN:0925-2312

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 November 2015

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Cited By

Song QYu QZhao ZLiu YAlsaadi F(2018)Dynamics of complex-valued neural networks with variable coefficients and proportional delaysNeurocomputing10.1016/j.neucom.2017.11.041275:C(2762-2768)Online publication date: 31-Jan-2018
https://dl.acm.org/doi/10.1016/j.neucom.2017.11.041
Hou NDong HWang ZRen WAlsaadi F(2017)H state estimation for discrete-time neural networks with distributed delays and randomly occurring uncertainties through Fading channelsNeural Networks10.1016/j.neunet.2016.12.00489:C(61-73)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1016/j.neunet.2016.12.004
Yu YDong HWang ZRen WAlsaadi F(2016)Design of non-fragile state estimators for discrete time-delayed neural networks with parameter uncertaintiesNeurocomputing10.1016/j.neucom.2015.11.079182:C(18-24)Online publication date: 19-Mar-2016
https://dl.acm.org/doi/10.1016/j.neucom.2015.11.079

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