期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 19, 期 2, 页码 366-370出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2007.910738
关键词
Cohen-Grossberg neural networks (CGNNs); delay-dependent criteria; linear matrix inequality (LMI); Markovian jumping; mixed delay
In this letter, the global asymptotical stability analysis problem is considered for a class of Markovian jumping stochastic Cohen-Grossberg neural networks (CGNNs) with mixed delays including discrete delays and distributed delays. An alternative delay-dependent stability analysis result is established based on the linear matrix inequality (LMI) technique, which can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Neither system transformation nor free-weight matrix via Newton-Leibniz formula is required. Two numerical examples are included to show the effectiveness of the result.
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