DELAY-DEPENDENT STABILITY CRITERION FOR BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH INTERVAL TIME-VARYING DELAYS
DELAY-DEPENDENT STABILITY CRITERION FOR BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH INTERVAL TIME-VARYING DELAYS
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DOI：
10.1142/s0217984909017807
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发表时间：
2009-01
影响因子：
1.9
通讯作者：
Ju H. Park;O. Kwon
中科院分区：
文献类型：
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作者：
Ju H. Park;O. Kwon
研究了具有时滞的双向联想记忆（BAM）神经网络的全局渐近稳定性。假设延迟是时变的，并且属于一个给定的区间。基于李雅普诺夫方法，提出了一种新的稳定性判据。该准则用线性矩阵不等式（LMI）来表示，可以很容易地用各种优化算法求解。通过两个数值算例说明了新结果的有效性。
In the letter, the global asymptotic stability of bidirectional associative memory (BAM) neural networks with delays is investigated. The delay is assumed to be time-varying and belongs to a given interval. A novel stability criterion for the stability is presented based on the Lyapunov method. The criterion is represented in terms of linear matrix inequality (LMI), which can be solved easily by various optimization algorithms. Two numerical examples are illustrated to show the effectiveness of our new result.