Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 23, Issue 2, Pages 199-210Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2011.2178563
Keywords
Dissipativity; stability; static neural networks; time delay
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Funding
- Hong Kong University Glaucoma Research Foundation [7137/09E]
- National Creative Research Groups Science Foundation of China [60721062]
- National Natural Science Foundation of China [60736021, 61174029]
- National High Technology Research and Development Program of China 863 Program [2008AA042902]
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This paper is concerned with the problems of stability and dissipativity analysis for static neural networks (NNs) with time delay. Some improved delay-dependent stability criteria are established for static NNs with time-varying or time-invariant delay using the delay partitioning technique. Based on these criteria, several delay-dependent sufficient conditions are given to guarantee the dissipativity of static NNs with time delay. All the given results in this paper are not only dependent upon the time delay but also upon the number of delay partitions. Some examples are given to illustrate the effectiveness and reduced conservatism of the proposed results.
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