{"title":"基于GA-GHNNs P的非线性系统最优辨识方法","authors":"Lin Xiao-fei, Weng Mu-yun","doi":"10.1109/ICCSIT.2009.5234495","DOIUrl":null,"url":null,"abstract":"Gaussian-Hopfield neural network algorithm (GHNNs) is the most commonly used method of solving the identification problem of nonlinear systems, but learning rule (LMS rule) is easy to fall into local optimum. Genetic algorithm (GA) has globally optimal ability and can solve the locally optimal problem well. This paper puts forward GA-GHNNs algorithm and uses GA algorithm to solve the optimum parameters of GHNNs network. And finally, it carries out simulation experiments to prove the validity of the algorithm. Simulation results also show that this method has the ability to distinguish nonlinear systems.","PeriodicalId":342396,"journal":{"name":"2009 2nd IEEE International Conference on Computer Science and Information Technology","volume":"34 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal identification method of nonlinear system based on GA-GHNNs P\",\"authors\":\"Lin Xiao-fei, Weng Mu-yun\",\"doi\":\"10.1109/ICCSIT.2009.5234495\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Gaussian-Hopfield neural network algorithm (GHNNs) is the most commonly used method of solving the identification problem of nonlinear systems, but learning rule (LMS rule) is easy to fall into local optimum. Genetic algorithm (GA) has globally optimal ability and can solve the locally optimal problem well. This paper puts forward GA-GHNNs algorithm and uses GA algorithm to solve the optimum parameters of GHNNs network. And finally, it carries out simulation experiments to prove the validity of the algorithm. Simulation results also show that this method has the ability to distinguish nonlinear systems.\",\"PeriodicalId\":342396,\"journal\":{\"name\":\"2009 2nd IEEE International Conference on Computer Science and Information Technology\",\"volume\":\"34 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 2nd IEEE International Conference on Computer Science and Information Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCSIT.2009.5234495\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 2nd IEEE International Conference on Computer Science and Information Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCSIT.2009.5234495","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Optimal identification method of nonlinear system based on GA-GHNNs P
Gaussian-Hopfield neural network algorithm (GHNNs) is the most commonly used method of solving the identification problem of nonlinear systems, but learning rule (LMS rule) is easy to fall into local optimum. Genetic algorithm (GA) has globally optimal ability and can solve the locally optimal problem well. This paper puts forward GA-GHNNs algorithm and uses GA algorithm to solve the optimum parameters of GHNNs network. And finally, it carries out simulation experiments to prove the validity of the algorithm. Simulation results also show that this method has the ability to distinguish nonlinear systems.