Delay-partitioning approach to stability analysis of generalized neural networks with time-varying delay via new integral inequality

On the basis of establishing a new integral inequality composed of a set of adjustable slack matrix variables, this paper mainly focuses on further improved stability criteria for a class of generalized neural networks (GNNs) with time-varying delay by delay-partitioning approach. A newly augmented...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2016-05, Vol.191, p.380-387
Main Authors: Chen, Zhi-Wen, Yang, Jun, Zhong, Shou-Ming
Format: Article
Language:English
Subjects:
Delay
Delay-partitioning approach
Derivatives
Enlargement
Generalized neural networks (GNNs)
Inequalities
Integral inequality
Integrals
Lyapunov–Krasovskii functional (LKF)
Mathematical models
Neural networks
Slack variables
Stability analysis
Time-varying delay
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Summary:On the basis of establishing a new integral inequality composed of a set of adjustable slack matrix variables, this paper mainly focuses on further improved stability criteria for a class of generalized neural networks (GNNs) with time-varying delay by delay-partitioning approach. A newly augmented Lyapunov–Krasovskii functional (LKF) containing triple-integral terms is constructed by decomposing integral interval. The new integral inequality together with Peng–Park׳s integral inequality and Free-Matrix-based integral inequality (FMII) is adopted to effectively reduce the enlargement in bounding the derivative of LKF. Therefore, less conservative results can be expected in terms of es and LMIs. Finally, two numerical examples are included to show that the proposed method is less conservative than existing ones.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2016.01.041