Stability analysis of delayed neural network using new delay-product based functionals

This paper concerns with stability analysis of neural networks with time varying delay. Two new delay-product type functionals (DPFs) are developed by introducing new states in the augmented vector of delay-product term. Then using these DPFs, two new Lyapunov-Krasovskii functionals (LKFs) are const...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 417; pp. 106 - 113
Main Authors Mahto, Sharat Chandra, Ghosh, Sandip, Saket, R.K., Nagar, Shyam Krishna
Format Journal Article
LanguageEnglish
Published Elsevier B.V 05.12.2020
Subjects
Delay-product functionals
Delayed neural network
Linear matrix inequality
Stability analysis
Time varying delay
Linear matrix inequality
Delay-product functionals
Stability analysis
Time varying delay
Delayed neural network
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Summary:This paper concerns with stability analysis of neural networks with time varying delay. Two new delay-product type functionals (DPFs) are developed by introducing new states in the augmented vector of delay-product term. Then using these DPFs, two new Lyapunov-Krasovskii functionals (LKFs) are constructed. Based on these LKFs, two delay-dependent stability criterion are obtained in the form of linear matrix inequalities. The effectiveness of the proposed criterion for delayed neural network is demonstrated by considering two examples.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.07.021