Stability analysis for Cohen-Grossberg neural networks with time-varying delays via LMI approach

Authors:
Chang-Hua Lien;Ker-Wei Yu;Yen-Feng Lin;Hao-Chin Chang;Yeong-Jay Chung
Affiliations:
Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan, ROC;Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan, ROC;Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan, ROC;Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan, ROC;Department of Marine Engineering, National Kaohsiung Marine University, Kaohsiung 811, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

The global asymptotic stability for a class of Cohen-Grossberg neural networks (CGNNs) with time-varying delays is investigated. Delay-independent and delay-dependent stability criteria are proposed to guarantee the robust stability and uniqueness of equilibrium point of CGNNs via LMI approach. Some numerical examples are illustrated to show the effectiveness of our results.