{"title":"Cluster synchronization of fractional-order two-layer networks and application in image encryption/decryption.","authors":"Juan Yu, Yanwei Yin, Tingting Shi, Cheng Hu","doi":"10.1016/j.neunet.2024.107023","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper, a type of fractional-order two-layer network model is constructed, wherein each layer in the network exhibits distinct topology. Subsequently, the cluster synchronization problem of fractional-order two-layer networks is investigated through a two-step approach. The initial step involves the implementation of finite-time cluster synchronization in the first layer by utilizing a fractional-order finite-time convergence lemma. Based upon this, the second step employs a novel approach of collectively treating the nodes within the same cluster in the first layer, thereby offering a significant insight for analyzing fractional-order two-layer networks cluster synchronization. In addition, the paper proposes a novel encryption/decryption scheme based on the cluster synchronization of fractional-order two-layer networks. By leveraging the complexity of chaotic sequences generated by fractional-order two-layer networks, the security of the encryption/decryption strategy is enhanced. Furthermore, three illustrative examples are provided to validate the theoretical findings.</p>","PeriodicalId":49763,"journal":{"name":"Neural Networks","volume":"184 ","pages":"107023"},"PeriodicalIF":6.0000,"publicationDate":"2024-12-11","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.2024.107023","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
In this paper, a type of fractional-order two-layer network model is constructed, wherein each layer in the network exhibits distinct topology. Subsequently, the cluster synchronization problem of fractional-order two-layer networks is investigated through a two-step approach. The initial step involves the implementation of finite-time cluster synchronization in the first layer by utilizing a fractional-order finite-time convergence lemma. Based upon this, the second step employs a novel approach of collectively treating the nodes within the same cluster in the first layer, thereby offering a significant insight for analyzing fractional-order two-layer networks cluster synchronization. In addition, the paper proposes a novel encryption/decryption scheme based on the cluster synchronization of fractional-order two-layer networks. By leveraging the complexity of chaotic sequences generated by fractional-order two-layer networks, the security of the encryption/decryption strategy is enhanced. Furthermore, three illustrative examples are provided to validate the theoretical findings.
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.