{"title":"一种新的连续时间系统混合进化算法","authors":"G. L. Santosuosso","doi":"10.1109/CDC.2001.980988","DOIUrl":null,"url":null,"abstract":"A novel evolutionary algorithm called atomic metaphor optimization strategy (AMOS) is proposed, which is designed for real-time analog optimization problems. This new evolutionary algorithm is integrated with the continuous time adaptive observer algorithm based on the Lyapunov stability theory, developed for classes of approximating functions with linear parametrization. The combined hybrid algorithm is applied to the online modeling of continuous-time nonlinear systems, via a nonlinearly parametrized neural approximation of the system dynamics.","PeriodicalId":131411,"journal":{"name":"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2001-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"AMOS-a new hybrid evolutionary algorithm for continuous time systems\",\"authors\":\"G. L. Santosuosso\",\"doi\":\"10.1109/CDC.2001.980988\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A novel evolutionary algorithm called atomic metaphor optimization strategy (AMOS) is proposed, which is designed for real-time analog optimization problems. This new evolutionary algorithm is integrated with the continuous time adaptive observer algorithm based on the Lyapunov stability theory, developed for classes of approximating functions with linear parametrization. The combined hybrid algorithm is applied to the online modeling of continuous-time nonlinear systems, via a nonlinearly parametrized neural approximation of the system dynamics.\",\"PeriodicalId\":131411,\"journal\":{\"name\":\"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)\",\"volume\":\"51 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2001-12-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.2001.980988\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.2001.980988","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
AMOS-a new hybrid evolutionary algorithm for continuous time systems
A novel evolutionary algorithm called atomic metaphor optimization strategy (AMOS) is proposed, which is designed for real-time analog optimization problems. This new evolutionary algorithm is integrated with the continuous time adaptive observer algorithm based on the Lyapunov stability theory, developed for classes of approximating functions with linear parametrization. The combined hybrid algorithm is applied to the online modeling of continuous-time nonlinear systems, via a nonlinearly parametrized neural approximation of the system dynamics.