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Global stability analysis of fractional-order Hopfield neural networks with time delay
Wang, Hu1; Yu, Yongguang1; Wen, Guoguang1; Zhang, Shuo1; Yu, Junzhi2
Source PublicationNEUROCOMPUTING
2015-04-22
Volume2015Issue:154Pages:15-23
SubtypeArticle
AbstractIn this paper, the global stability analysis of fractional-order Hopfield neural networks with time delay is investigated. A stability theorem for linear fractional-order systems with time delay is presented. And, a comparison theorem for a class of fractional-order systems with time delay is shown. The existence and uniqueness of the equilibrium point for fractional-order Hopfield neural networks with time delay are proved. Furthermore, the global asymptotic stability conditions of fractional-order neural networks with time delay are obtained. Finally, a numerical example is given to illustrate the effectiveness of the theoretical results.
KeywordHopfield Neural Networks Global Stability Fractional-order Time Delay
WOS HeadingsScience & Technology ; Technology
WOS KeywordDIFFERENTIAL-EQUATIONS ; DIFFUSION ; DYNAMICS ; SYSTEM ; CHAOS ; SYNCHRONIZATION ; CALCULUS ; NEURONS
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000350081900002
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/8085
Collection复杂系统管理与控制国家重点实验室_先进机器人
Corresponding AuthorYu, Yongguang
Affiliation1. Department of Mathematics, Beijing Jiaotong University, Beijing, China
2.Institute of Automation, Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Wang, Hu,Yu, Yongguang,Wen, Guoguang,et al. Global stability analysis of fractional-order Hopfield neural networks with time delay[J]. NEUROCOMPUTING,2015,2015(154):15-23.
APA Wang, Hu,Yu, Yongguang,Wen, Guoguang,Zhang, Shuo,&Yu, Junzhi.(2015).Global stability analysis of fractional-order Hopfield neural networks with time delay.NEUROCOMPUTING,2015(154),15-23.
MLA Wang, Hu,et al."Global stability analysis of fractional-order Hopfield neural networks with time delay".NEUROCOMPUTING 2015.154(2015):15-23.
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