{"title":"参数不匹配的延迟随机四元数值模糊蜂窝神经网络的准投影同步控制","authors":"Xiaofang Meng, Yu Fei, Zhouhong Li","doi":"10.1007/s12559-024-10299-9","DOIUrl":null,"url":null,"abstract":"<p>This paper deals with the quasi-projective synchronization problem of delayed stochastic quaternion fuzzy cellular neural networks with mismatch parameters. Although the parameter mismatch of the drive-response system increases the computational complexity of the article, it is of practical significance to consider the existence of deviations between the two systems. The method of this article is to design an appropriate controller and construct Lyapunov functional and stochastic analysis theory based on the Itô formula in the quaternion domain. We adopt the non-decomposable method of quaternion FCNN, which preserves the original data and reduces computational effort. We obtain sufficient conditions for quasi-projective synchronization of the considered random quaternion numerical FCNNs with mismatched parameters. Additionally, we estimate the error bounds of quasi-projective synchronization and then carry out a numerical example to verify their validity. Our results are novel even if the considered neural networks degenerate into real-valued or complex-valued neural networks. This article provides a good research idea for studying the quasi-projective synchronization problem of random quaternion numerical FCNN with time delay and has obtained good results. The method in this article can also be used to study the quasi-projective synchronization of a Clifford-valued neural network.</p>","PeriodicalId":51243,"journal":{"name":"Cognitive Computation","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quasi-projective Synchronization Control of Delayed Stochastic Quaternion-Valued Fuzzy Cellular Neural Networks with Mismatched Parameters\",\"authors\":\"Xiaofang Meng, Yu Fei, Zhouhong Li\",\"doi\":\"10.1007/s12559-024-10299-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>This paper deals with the quasi-projective synchronization problem of delayed stochastic quaternion fuzzy cellular neural networks with mismatch parameters. Although the parameter mismatch of the drive-response system increases the computational complexity of the article, it is of practical significance to consider the existence of deviations between the two systems. The method of this article is to design an appropriate controller and construct Lyapunov functional and stochastic analysis theory based on the Itô formula in the quaternion domain. We adopt the non-decomposable method of quaternion FCNN, which preserves the original data and reduces computational effort. We obtain sufficient conditions for quasi-projective synchronization of the considered random quaternion numerical FCNNs with mismatched parameters. Additionally, we estimate the error bounds of quasi-projective synchronization and then carry out a numerical example to verify their validity. Our results are novel even if the considered neural networks degenerate into real-valued or complex-valued neural networks. This article provides a good research idea for studying the quasi-projective synchronization problem of random quaternion numerical FCNN with time delay and has obtained good results. The method in this article can also be used to study the quasi-projective synchronization of a Clifford-valued neural network.</p>\",\"PeriodicalId\":51243,\"journal\":{\"name\":\"Cognitive Computation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cognitive Computation\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s12559-024-10299-9\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cognitive Computation","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s12559-024-10299-9","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Quasi-projective Synchronization Control of Delayed Stochastic Quaternion-Valued Fuzzy Cellular Neural Networks with Mismatched Parameters
This paper deals with the quasi-projective synchronization problem of delayed stochastic quaternion fuzzy cellular neural networks with mismatch parameters. Although the parameter mismatch of the drive-response system increases the computational complexity of the article, it is of practical significance to consider the existence of deviations between the two systems. The method of this article is to design an appropriate controller and construct Lyapunov functional and stochastic analysis theory based on the Itô formula in the quaternion domain. We adopt the non-decomposable method of quaternion FCNN, which preserves the original data and reduces computational effort. We obtain sufficient conditions for quasi-projective synchronization of the considered random quaternion numerical FCNNs with mismatched parameters. Additionally, we estimate the error bounds of quasi-projective synchronization and then carry out a numerical example to verify their validity. Our results are novel even if the considered neural networks degenerate into real-valued or complex-valued neural networks. This article provides a good research idea for studying the quasi-projective synchronization problem of random quaternion numerical FCNN with time delay and has obtained good results. The method in this article can also be used to study the quasi-projective synchronization of a Clifford-valued neural network.
期刊介绍:
Cognitive Computation is an international, peer-reviewed, interdisciplinary journal that publishes cutting-edge articles describing original basic and applied work involving biologically-inspired computational accounts of all aspects of natural and artificial cognitive systems. It provides a new platform for the dissemination of research, current practices and future trends in the emerging discipline of cognitive computation that bridges the gap between life sciences, social sciences, engineering, physical and mathematical sciences, and humanities.