具有时滞的不连续复值BAM神经网络的固定时间同步

IF 6.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Qian Wang , Yinjie Qian , Yuanhua Qiao
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引用次数: 0

摘要

研究了具有时滞的不连续复值双向联想记忆神经网络的固定时间同步问题。首先,将cvbamnn分解为实部和虚部,构造等效实值子系统进行分析。然后,设计了两种不连续反馈控制器,构造了基于p-范数和1-范数的两种Lyapunov函数,研究了cvbamnn的傅里叶变换特性。利用微分包裹体理论和一些不等式技术,建立了一些较近代的判据,并较为明确地估计了凝固时间的上界。最后,通过数值模拟验证了理论结果的有效性和适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fixed-time synchronization of discontinuous complex-valued BAM neural networks with time delays
The fixed-time synchronization (FTS) of discontinuous complex-valued bidirectional associative memory neural networks (CVBAMNNs) with time delays is investigated in this paper. First, CVBAMNNs are separated into real and imaginary parts, the equivalent real-valued subsystems are constructed for the analysis. Then, two kinds of discontinuous feedback controllers are designed, and two forms of Lyapunov function based on p-norm and 1-norm are constructed to study the FTS of CVBAMNNs. By using differential inclusions theory and some inequality techniques, some neoteric criteria are established to achieve FTS, and the upper-bound of the setting time is more explicitly estimated as well. Finally, numerical simulations are presented to verify the effectiveness and applicability of the developed theoretical results.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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