基于对偶神经网络的微分进化算法优化分数阶超混沌系统的有限时间滑模同步

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Networks Pub Date : 2022-10-30 DOI:10.1049/ntw2.12069
Keyong Shao, Ao Feng, Tingting Wang, Wenju Li, Jilu Jiang
{"title":"基于对偶神经网络的微分进化算法优化分数阶超混沌系统的有限时间滑模同步","authors":"Keyong Shao,&nbsp;Ao Feng,&nbsp;Tingting Wang,&nbsp;Wenju Li,&nbsp;Jilu Jiang","doi":"10.1049/ntw2.12069","DOIUrl":null,"url":null,"abstract":"<p>To solve the synchronisation problem associated with fractional-order hyperchaotic systems, in this study, a new dual-neural network finite-time sliding mode control method was developed, and a differential evolution algorithm was used to optimise the switching gain, control parameters, and sliding mode surface parameters, greatly reducing chattering problems in sliding mode controllers. By using the developed method, the complete synchronisation of the drive system and the response system of a fractional-order hyperchaotic system was realised in a finite time; moreover, the stability of the error system under this method was proved by using Lyapunov stability theorem. Numerical simulation results verified the feasibility and superiority of the method.</p>","PeriodicalId":46240,"journal":{"name":"IET Networks","volume":null,"pages":null},"PeriodicalIF":1.3000,"publicationDate":"2022-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.12069","citationCount":"0","resultStr":"{\"title\":\"Finite-time sliding mode synchronisation of a fractional-order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks\",\"authors\":\"Keyong Shao,&nbsp;Ao Feng,&nbsp;Tingting Wang,&nbsp;Wenju Li,&nbsp;Jilu Jiang\",\"doi\":\"10.1049/ntw2.12069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To solve the synchronisation problem associated with fractional-order hyperchaotic systems, in this study, a new dual-neural network finite-time sliding mode control method was developed, and a differential evolution algorithm was used to optimise the switching gain, control parameters, and sliding mode surface parameters, greatly reducing chattering problems in sliding mode controllers. By using the developed method, the complete synchronisation of the drive system and the response system of a fractional-order hyperchaotic system was realised in a finite time; moreover, the stability of the error system under this method was proved by using Lyapunov stability theorem. Numerical simulation results verified the feasibility and superiority of the method.</p>\",\"PeriodicalId\":46240,\"journal\":{\"name\":\"IET Networks\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2022-10-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1049/ntw2.12069\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IET Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1049/ntw2.12069\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Networks","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/ntw2.12069","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

为了解决分数阶超混沌系统的同步问题,本研究提出了一种新的双神经网络有限时间滑模控制方法,并采用微分进化算法对切换增益、控制参数和滑模表面参数进行优化,极大地减少了滑模控制器的抖振问题。利用该方法,在有限时间内实现了分数阶超混沌系统驱动系统与响应系统的完全同步;利用李雅普诺夫稳定性定理证明了该方法下误差系统的稳定性。数值仿真结果验证了该方法的可行性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Finite-time sliding mode synchronisation of a fractional-order hyperchaotic system optimised using a differential evolution algorithm with dual neural networks

To solve the synchronisation problem associated with fractional-order hyperchaotic systems, in this study, a new dual-neural network finite-time sliding mode control method was developed, and a differential evolution algorithm was used to optimise the switching gain, control parameters, and sliding mode surface parameters, greatly reducing chattering problems in sliding mode controllers. By using the developed method, the complete synchronisation of the drive system and the response system of a fractional-order hyperchaotic system was realised in a finite time; moreover, the stability of the error system under this method was proved by using Lyapunov stability theorem. Numerical simulation results verified the feasibility and superiority of the method.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IET Networks
IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
5.00
自引率
0.00%
发文量
41
审稿时长
33 weeks
期刊介绍: IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.
期刊最新文献
Common criteria for security evaluation and malicious intrusion detection mechanism of dam supervisory control and data acquisition system Energy and throughput efficient mobile wireless sensor networks: A deep reinforcement learning approach Disaster scenario optimised link state routing protocol and message prioritisation A PU-learning based approach for cross-site scripting attacking reality detection Enhanced multivariate singular spectrum analysis-based network traffic forecasting for real time industrial IoT applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1