Novel LMI-Based Condition on Global Asymptotic Stability for a Class of Cohen–Grossberg BAM Networks With Extended Activation Functions
Novel LMI-Based Condition on Global Asymptotic Stability for a Class of Cohen–Grossberg BAM Networks With Extended Activation Functions
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DOI：
10.1109/tnnls.2013.2289855
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发表时间：
2014-06
影响因子：
10.4
通讯作者：
Zhengqiu Zhang;Jinde Cao;Dongming Zhou
中科院分区：
文献类型：
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作者：
Zhengqiu Zhang;Jinde Cao;Dongming Zhou
研究了一类具有时滞的Cohen-Grossberg双向联想记忆（BAM）神经网络的全局渐近稳定性。在激励函数仅满足广义Lipschitz条件，行为函数仅满足全局Lipschitz条件的假设下，利用线性矩阵不等式（LMI）方法和同胚理论，给出了一类神经网络全局渐近稳定的充分条件.在我们的结果中，激活函数的广义全局Lipschitz条件比有界性和单调性的假设保守性更小，比一般全局Lipschitz条件的假设更弱，行为函数的全局Lipschitz条件也比已有文献中单调性和可微性的假设保守性更小.
This paper is concerned with global asymptotic stability of a class of Cohen-Grossberg bidirectional associative memory (BAM) neural networks with delays. Under the assumptions that the activation functions only satisfy the so-called extended global Lipschitz condition and the behaved functions only satisfy global Lipschitz condition, we apply linear matrix inequality (LMI) method and homeomorphism theory to propose a new LMI-based sufficient condition for global asymptotic stability of the concerned neural networks. In our results, the extended global Lipschitz condition on the activation functions is less conservative than the assumptions for boundedness and monotonicity and is weaker than the assumption for the general global Lipschitz condition, the global Lipschitz condition on the behaved functions is also less conservative than the assumptions for monotonicity and differentiability in existing papers.