New LMI-based condition on global asymptotic stability concerning BAM neural networks of neutral type

Authors:
Zhengqiu Zhang;Kaiyu Liu;Yan Yang
Affiliations:
College of Mathematics, Hunan University, Changsha, 410082, PR China;College of Mathematics, Hunan University, Changsha, 410082, PR China;College of Mathematics, Hunan University, Changsha, 410082, PR China
Venue:
Neurocomputing
Year:
2012

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Abstract

In this paper, we discuss global asymptotic stability to a BAM neural networks of neutral type with delays. Under the assumptions that the activation functions only satisfy global Lipschitz conditions, a new and complicated LMI condition is established on global asymptotic stability for the above neutral neural networks by means of using Homeomorphism theory, matrix and Lyapunov functional. In our result, the hypotheses for boundedness in [20,21] and monotonicity in [20] on the activation functions are removed. On the other hand, the LMI condition is also different from those in [20,21]. Finally, an example is given to show the effectiveness of the theoretical result.