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时滞分数阶竞争神经网络的多稳定性。

Multistability of delayed fractional-order competitive neural networks.

机构信息

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China; Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China, Wuhan, China.

Science Program, Texas A&M University at Qatar, Doha, Qatar.

出版信息

Neural Netw. 2021 Aug;140:325-335. doi: 10.1016/j.neunet.2021.03.036. Epub 2021 Apr 8.

Abstract

This paper is concerned with the multistability of fractional-order competitive neural networks (FCNNs) with time-varying delays. Based on the division of state space, the equilibrium points (EPs) of FCNNs are given. Several sufficient conditions and criteria are proposed to ascertain the multiple O(t)-stability of delayed FCNNs. The O(t)-stability is the extension of Mittag-Leffler stability of fractional-order neural networks, which contains monostability and multistability. Moreover, the attraction basins of the stable EPs of FCNNs are estimated, which shows the attraction basins of the stable EPs can be larger than the divided subsets. These conditions and criteria supplement and improve the previous results. Finally, the results are illustrated by the simulation examples.

摘要

本文研究了时变时滞分数阶竞争神经网络(FCNNs)的多稳定性。基于状态空间的划分,给出了 FCNN 的平衡点(EPs)。提出了几个充分条件和准则来确定时滞 FCNN 的多个 O(t)-稳定性。O(t)-稳定性是分数阶神经网络的 Mittag-Leffler 稳定性的扩展,包含单稳定性和多稳定性。此外,还估计了 FCNN 稳定平衡点的吸引域,表明稳定平衡点的吸引域可以大于划分的子集。这些条件和准则补充和完善了以前的结果。最后,通过仿真示例说明了结果。

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