期刊
NEURAL COMPUTING & APPLICATIONS
卷 31, 期 7, 页码 2717-2726出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-017-3224-0
关键词
Generalized BAM neural network; Exponential stability; Passivity and dissipativity analysis; Time-varying delay; Weighted integral inequality
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2016R1A6A1A03013567]
- Korea government (MEST) [NRF-2015R1A2A2A05001610]
- Thailand Research Fund (TRF), Thailand
This article evaluates the exponential dissipativity and passivity criterions for generalized bidirectional associative memory neural networks (BAMGNNs) including interval time-varying delayed signals. Exponential dissipativity and passivity criterions are proposed by making suitable Lyapunov-Krasovskii functional and proposing a novel approach. The improved reciprocally convex combination and weighted integral inequality techniques are utilized to obtain new exponential dissipativity and passivity conditions of such delayed BAMGNNs. The feasibility of the obtained results is clearly demonstrated by numerical examples.
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