{"title":"基于监督神经气体算法的非线性动态辨识","authors":"Iván Machón-González, Hilario López-García","doi":"10.1109/WSOM.2017.8020031","DOIUrl":null,"url":null,"abstract":"The dynamic identification of a nonlinear plant is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function demonstrating that the local linear approximation can be done. Moreover, other parameters are analyzed in order to obtain a correct modeling.","PeriodicalId":130086,"journal":{"name":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonlinear dynamic identification using supervised neural gas algorithm\",\"authors\":\"Iván Machón-González, Hilario López-García\",\"doi\":\"10.1109/WSOM.2017.8020031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The dynamic identification of a nonlinear plant is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function demonstrating that the local linear approximation can be done. Moreover, other parameters are analyzed in order to obtain a correct modeling.\",\"PeriodicalId\":130086,\"journal\":{\"name\":\"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WSOM.2017.8020031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WSOM.2017.8020031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nonlinear dynamic identification using supervised neural gas algorithm
The dynamic identification of a nonlinear plant is not a trivial issue. The application of a neural gas network that is trained with a supervised batch version of the algorithm can produce identification models in a robust way. In this paper, the neural model identifies each local transfer function demonstrating that the local linear approximation can be done. Moreover, other parameters are analyzed in order to obtain a correct modeling.
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