{"title":"Outer synchronization and outer H<sub>∞</sub> synchronization for coupled fractional-order reaction-diffusion neural networks with multiweights.","authors":"Jin-Liang Wang, Si-Yang Wang, Yan-Ran Zhu, Tingwen Huang","doi":"10.1016/j.neunet.2024.106893","DOIUrl":null,"url":null,"abstract":"<p><p>This paper introduces multiple state or spatial-diffusion coupled fractional-order reaction-diffusion neural networks, and discusses the outer synchronization and outer H<sub>∞</sub> synchronization problems for these coupled fractional-order reaction-diffusion neural networks (CFRNNs). The Lyapunov functional method, Laplace transform and inequality techniques are utilized to obtain some outer synchronization conditions for CFRNNs. Moreover, some criteria are also provided to make sure the outer H<sub>∞</sub> synchronization of CFRNNs. Finally, the derived outer and outer H<sub>∞</sub> synchronization conditions are validated on the basis of two numerical examples.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"181 ","pages":"106893"},"PeriodicalIF":6.0000,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1016/j.neunet.2024.106893","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This paper introduces multiple state or spatial-diffusion coupled fractional-order reaction-diffusion neural networks, and discusses the outer synchronization and outer H∞ synchronization problems for these coupled fractional-order reaction-diffusion neural networks (CFRNNs). The Lyapunov functional method, Laplace transform and inequality techniques are utilized to obtain some outer synchronization conditions for CFRNNs. Moreover, some criteria are also provided to make sure the outer H∞ synchronization of CFRNNs. Finally, the derived outer and outer H∞ synchronization conditions are validated on the basis of two numerical examples.
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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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