标题:Further stability analysis for delayed complex-valued recurrent neural networks
作者:Zhang, Ziye; Liu, Xiaoping; Chen, Jian; Guo, Runan; Zhou, Shaowei
作者机构:[Zhang, Ziye; Guo, Runan; Zhou, Shaowei] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China.; [Liu, Xiaoping] Shando 更多
通讯作者:Zhang, Ziye
通讯作者地址:[Zhang, ZY]Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China.
来源:NEUROCOMPUTING
出版年:2017
卷:251
页码:81-89
DOI:10.1016/j.neucom.2017.04.013
关键词:Complex-valued neural networks; Global stability; Time-delay
摘要:This paper focuses on the stability problem for delayed complex-valued recurrent neural networks. Whether the complex-valued activation functions are explicitly expressed by separating real and imaginary parts or not, they are always assumed to satisfy the globally Lipschitz condition in the complex domain. For two cases of the activation functions, based on the homeomorphism theory and Lyapunov function approach new delay-dependent sufficient conditions to guarantee the existence, uniqueness, and globally asymptotical stability of the equilibrium point of system are obtained, respectively. For each case, several numerical examples are given to show the effectiveness and the advantages of the obtained results. (C) 2017 Elsevier B.V. All rights reserved.
收录类别:EI;SCIE
WOS核心被引频次:4
资源类型:期刊论文
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