Stability analysis for uncertain switched delayed complex-valued neural networks

Nallappan Gunasekaran; Guisheng Zhai

doi:10.1016/j.neucom.2019.08.030

Stability analysis for uncertain switched delayed complex-valued neural networks

Nallappan Gunasekaran, Guisheng Zhai

Research output: Contribution to journal › Article › peer-review

27 Citations (Scopus)

Abstract

The main concern of the paper is to address the stability of switched delayed complex-valued neural networks with uncertainties. Based on suitable Lyapunov–Krasovskii functional (LKF) and proposed lemma, the delay-dependent sufficient conditions are derived to guarantee the asymptotical stability of considered uncertain switched complex-valued neural networks. The derived sufficient conditions in terms of linear matrix inequalities are solved with the help of YALMIP toolbox in MATLAB. Two numerical examples are provided to ensure the effectiveness of the theoretical conditions.

Original language	English
Pages (from-to)	198-206
Number of pages	9
Journal	Neurocomputing
Volume	367
DOIs	https://doi.org/10.1016/j.neucom.2019.08.030
Publication status	Published - 2019 Nov 20

Keywords

Complex-valued neural networks
Integral inequality
Linear matrix inequality
Lyapunov method
Stability

ASJC Scopus subject areas

Computer Science Applications
Cognitive Neuroscience
Artificial Intelligence

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10.1016/j.neucom.2019.08.030

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title = "Stability analysis for uncertain switched delayed complex-valued neural networks",

abstract = "The main concern of the paper is to address the stability of switched delayed complex-valued neural networks with uncertainties. Based on suitable Lyapunov–Krasovskii functional (LKF) and proposed lemma, the delay-dependent sufficient conditions are derived to guarantee the asymptotical stability of considered uncertain switched complex-valued neural networks. The derived sufficient conditions in terms of linear matrix inequalities are solved with the help of YALMIP toolbox in MATLAB. Two numerical examples are provided to ensure the effectiveness of the theoretical conditions.",

keywords = "Complex-valued neural networks, Integral inequality, Linear matrix inequality, Lyapunov method, Stability",

author = "Nallappan Gunasekaran and Guisheng Zhai",

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AU - Zhai, Guisheng

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N2 - The main concern of the paper is to address the stability of switched delayed complex-valued neural networks with uncertainties. Based on suitable Lyapunov–Krasovskii functional (LKF) and proposed lemma, the delay-dependent sufficient conditions are derived to guarantee the asymptotical stability of considered uncertain switched complex-valued neural networks. The derived sufficient conditions in terms of linear matrix inequalities are solved with the help of YALMIP toolbox in MATLAB. Two numerical examples are provided to ensure the effectiveness of the theoretical conditions.

AB - The main concern of the paper is to address the stability of switched delayed complex-valued neural networks with uncertainties. Based on suitable Lyapunov–Krasovskii functional (LKF) and proposed lemma, the delay-dependent sufficient conditions are derived to guarantee the asymptotical stability of considered uncertain switched complex-valued neural networks. The derived sufficient conditions in terms of linear matrix inequalities are solved with the help of YALMIP toolbox in MATLAB. Two numerical examples are provided to ensure the effectiveness of the theoretical conditions.

KW - Complex-valued neural networks

KW - Integral inequality

KW - Linear matrix inequality

KW - Lyapunov method

KW - Stability

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JO - Neurocomputing

JF - Neurocomputing

ER -

Stability analysis for uncertain switched delayed complex-valued neural networks

Abstract

Keywords

ASJC Scopus subject areas

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