A Novel Delay-Product-Type Functional Method To Extended Dissipativity Analysis For Markovian Jump Neural Networks
IEEE ACCESS(2021)
摘要
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.
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关键词
Linear matrix inequalities, Symmetric matrices, Delays, Biological neural networks, Artificial neural networks, Time-varying systems, Couplings, Markovian jump neural networks, extended dissipativity, time-varying delay, delay-product-type functional, S-procedure lemma
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