A Novel Delay-Product-Type Functional Method to Extended Dissipativity Analysis for Markovian Jump Neural Networks

This paper studies the problem of extended dissipativity analysis for Markovian jump neural networks (MJNNs) with time-varying delay. Combining Wirtinger-based double integral inequality and S-procedure lemma, a novel double integral-based delay-product-type (DIDPT) Lyapunov functional is constructe...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.20170-20178
Main Authors: Huang, Xiaoping, Wu, Caiyun, Wang, Yuzhong, Li, Wendong
Format: Article
Language:English
Subjects:
Artificial neural networks
Biological neural networks
Couplings
Delay
delay-product-type functional
Delays
extended dissipativity
Integrals
Linear matrix inequalities
Markovian jump neural networks
Neural networks
S-procedure lemma
Symmetric matrices
time-varying delay
Time-varying systems
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Summary:This paper studies the problem of extended dissipativity analysis for Markovian jump neural networks (MJNNs) with time-varying delay. Combining Wirtinger-based double integral inequality and S-procedure lemma, a novel double integral-based delay-product-type (DIDPT) Lyapunov functional is constructed in this paper, which avoids the incomplete components in the existing works. Then, based on parameter-dependent reciprocally convex inequality (PDRCI) and the novel DIDPT, a new extended dissipativity condition is obtained for MJNNs. A numerical example is employed to illustrate the advantages of the proposed method.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3054770