Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 49, Issue 5, Pages 1803-1815Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2018.2813979
Keywords
Multiple psi-type stability; recurrent neural networks (RNNs); robustness; time-varying delays
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Funding
- Guangdong Innovative and Entrepreneurial Research Team Program [2014ZT05G304]
- Natural Science Foundation of China [61673188, 61761130081]
- National Key Research and Development Program of China [2016YFB0800402]
- Foundation for Innovative Research Groups of Hubei Province of China [2017CFA005]
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In this paper, the psi-type stability and robustness of recurrent neural networks are investigated by using the differential inequality. By utilizing psi-type functions combined with the inequality techniques, some sufficient conditions ensuring psi-type stability and robustness are derived for linear neural networks with time-varying delays. Then, by choosing appropriate Lipschitz coefficient in subregion, some algebraic criteria of the multiple psi-type stability and robust boundedness are established for the delayed neural networks with time-varying delays. For special cases, several criteria are also presented by selecting parameters with easy implementation. The derived results cover both psi-type mono-stability and multiple psi-type stability. In addition, these theoretical results contain exponential stability, polynomial stability, and mu-stability, and they also complement and extend some previous results. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed criteria.
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