{"title":"多分辨率小波最小二乘支持向量机网络用于非线性系统建模","authors":"T. Mahmoud","doi":"10.1109/MMAR.2010.5587200","DOIUrl":null,"url":null,"abstract":"This paper proposes Multi resolution Wavelet Least Squares Support Vector Machine (MRWLS-SVM) network for the nonlinear system modeling. It composes a set of sub-wavelet least squares support vector machine networks with specified resolutions. The outputs of these sub-networks are aggregated via a set of weights to produce the network output. The structure of the MRWLS-SVM network is achieved using the fuzzy c-mean and the least squares support vector algorithm. The stability of the proposed network has been investigated using Lypunov stability theorem. The generalization of the proposed MRWLS-SVM network for the modeling purpose has investigated using two nonlinear systems.","PeriodicalId":336219,"journal":{"name":"2010 15th International Conference on Methods and Models in Automation and Robotics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Multi resolution wavelet least squares support vector machine network for nonlinear system modeling\",\"authors\":\"T. Mahmoud\",\"doi\":\"10.1109/MMAR.2010.5587200\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes Multi resolution Wavelet Least Squares Support Vector Machine (MRWLS-SVM) network for the nonlinear system modeling. It composes a set of sub-wavelet least squares support vector machine networks with specified resolutions. The outputs of these sub-networks are aggregated via a set of weights to produce the network output. The structure of the MRWLS-SVM network is achieved using the fuzzy c-mean and the least squares support vector algorithm. The stability of the proposed network has been investigated using Lypunov stability theorem. The generalization of the proposed MRWLS-SVM network for the modeling purpose has investigated using two nonlinear systems.\",\"PeriodicalId\":336219,\"journal\":{\"name\":\"2010 15th International Conference on Methods and Models in Automation and Robotics\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 15th International Conference on Methods and Models in Automation and Robotics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMAR.2010.5587200\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 15th International Conference on Methods and Models in Automation and Robotics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMAR.2010.5587200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multi resolution wavelet least squares support vector machine network for nonlinear system modeling
This paper proposes Multi resolution Wavelet Least Squares Support Vector Machine (MRWLS-SVM) network for the nonlinear system modeling. It composes a set of sub-wavelet least squares support vector machine networks with specified resolutions. The outputs of these sub-networks are aggregated via a set of weights to produce the network output. The structure of the MRWLS-SVM network is achieved using the fuzzy c-mean and the least squares support vector algorithm. The stability of the proposed network has been investigated using Lypunov stability theorem. The generalization of the proposed MRWLS-SVM network for the modeling purpose has investigated using two nonlinear systems.