Comparison principles and stability of nonlinear fractional-order cellular neural networks with multiple time delays

Comparison principles and stability of nonlinear fractional-order cellular neural networks with multiple time delays
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多时滞非线性分数阶细胞神经网络的比较原理及稳定性

DOI:
10.1016/j.neucom.2015.05.063
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发表时间:
2015-11
期刊:
影响因子:
6
通讯作者:
Chen Liping
Chen Liping
中科院分区:
计算机科学2区
文献类型:
--
作者:
Liang Song;Wu Ranchao;Chen Liping

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在证明了mitag - leffler函数为正的基础上,建立了具有多时滞的分数阶线性系统的一些比较原理。然后利用分数阶线性时滞系统的稳定性理论,证明了具有多个时滞的比较系统在一定条件下是渐近稳定的。根据比较结果,原系统的渐近稳定性推导自比较系统的渐近稳定性。然后将所得结果应用于多时滞非线性分数阶细胞神经网络的渐近稳定性研究。针对Lyapunov函数的分数阶导数所满足的不等式,给出了分数阶神经模型渐近稳定性的若干保证准则。通过数值模拟验证了所提稳定性准则的有效性和可行性。
Some comparison principles for fractional-order linear systems with multiple time delays are established, after Mittag–Leffler functions are showed to be positive. Then by the stability theory of fractional linear delayed systems, the comparison system with multiple time delays is showed to be asymptotically stable under some conditions. Based on the comparison results, the asymptotical stability of the original systems follows from that of the comparison system. Then the obtained results are applied to investigate the asymptotical stability of nonlinear fractional-order cellular neural networks with multiple time delays. In terms of the inequality satisfied by the fractional derivative of Lyapunov function, some criteria ensuring asymptotical stability of fractional neural models are derived. Numerical simulations are presented to demonstrate the validity and feasibility of the proposed stability criteria.
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