Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 32, Issue 7, Pages 3240-3246Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2020.3008691
Keywords
Delays; Artificial neural networks; Delay effects; Learning systems; Symmetric matrices; Pattern recognition; Delay-product-type (DPT) functional; extended dissipativity; Markovian jump neural networks (MJNNs); time-varying delay
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Funding
- National Natural Science Foundation of China [61973070]
- Liaoning Revitalization Talents Program [XLYC1802010]
- SAPI Fundamental Research Funds [2018ZCX22]
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This study focuses on extended dissipativity analysis for MJNNs with time-varying delay, by constructing a DIDPT Lyapunov functional and removing unnecessary constraints to obtain more general results. The advantages of the proposed method are illustrated through a numerical example.
This brief studies the problem of extended dissipativity analysis for the Markovian jump neural networks (MJNNs) with time-varying delay. A double-integral-based delay-product-type (DIDPT) Lyapunov functional is first constructed in this brief, which makes full use of the information of time delay. Moreover, some unnecessary constraints on the system structure are removed, which leads to more general results. A numerical example is employed to illustrate the advantages of the proposed method.
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