Finite-time cluster synchronization of multi-weighted fractional-order coupled neural networks with and without impulsive effects

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

Abstract

In this paper, finite-time cluster synchronization (FTCS) of multi-weighted fractional-order neural networks is studied. Firstly, a FTCS criterion of the considered neural networks is obtained by designing a new delayed state feedback controller. Secondly, a FTCS criterion for the considered neural networks with mixed impulsive effects is given by constructing a new piecewise controller, where both synchronizing and desynchronizing impulses are taken into account. It should be noted that it is the first time that finite-time cluster synchronization of multi-weighted neural networks has been investigated. Finally, numerical simulations are given to show the validity of the theoretical results.

有脉冲效应和无脉冲效应的多加权分数阶耦合神经网络的有限时间群同步。
本文研究了多权分阶神经网络的有限时间群同步(FTCS)。首先,通过设计一种新的延迟状态反馈控制器,获得了所考虑的神经网络的 FTCS 准则。其次,通过构建一个新的片式控制器,同时考虑同步和非同步脉冲,给出了所考虑的具有混合脉冲效应的神经网络的 FTCS 准则。需要指出的是,这是首次对多权重神经网络的有限时间群同步进行研究。最后,还给出了数值模拟,以证明理论结果的正确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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