Finite-time stability analysis for fractional-order Cohen–Grossberg BAM neural networks with time delays
Finite-time stability analysis for fractional-order Cohen–Grossberg BAM neural networks with time delays
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DOI：
10.1007/s00521-016-2641-9
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发表时间：
2016-11
影响因子：
6
通讯作者：
R. Chinnathambi;Fathalla A. Rihan;S. Lakshmanan;M. Palanisamy
中科院分区：
文献类型：
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作者：
R. Chinnathambi;Fathalla A. Rihan;S. Lakshmanan;M. Palanisamy
本文研究了一类时滞分数阶 Cohen-Grossberg BAM 神经网络的有限时间稳定性问题。利用一些不等式技术、微分中值定理和收缩映射原理，给出了确保此类分数阶神经模型的有限时间稳定性的充分条件。最后，提供了数值例子和模拟来证明所得出的理论结果的有效性。
In this paper, the problem of finite-time stability for a class of fractional-order Cohen–Grossberg BAM neural networks with time delays is investigated. Using some inequality techniques, differential mean value theorem and contraction mapping principle, sufficient conditions are presented to ensure the finite-time stability of such fractional-order neural models. Finally, a numerical example and simulations are provided to demonstrate the effectiveness of the derived theoretical results.