{"title":"基于局部条件的资源分配网络及其在非线性系统预测中的应用","authors":"Wenyuan Qi, Dazi Li, Q. Jin","doi":"10.1109/WCICA.2012.6357892","DOIUrl":null,"url":null,"abstract":"In this paper, a resource-allocating network based on local conditions (RAN-LC) is proposed to avoid the existing problems of RAN. This method gets the initial hidden nodes by using K-means clustering algorithm and the characteristics of activation function, and it utilizes new Novelty Criterion based on local conditions instead of the old one to keep the network neat and efficient. Moreover, it adopts Multi-patterns to enhance the generalization ability of network in the state of parameters adjustment. The simulation results show that this method can generate network quickly and more reasonable. The network generated finally has good performance and also works well in the prediction of nonlinear systems.","PeriodicalId":114901,"journal":{"name":"Proceedings of the 10th World Congress on Intelligent Control and Automation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A resource-allocating network based on local conditions and its application in prediction of nonlinear systems\",\"authors\":\"Wenyuan Qi, Dazi Li, Q. Jin\",\"doi\":\"10.1109/WCICA.2012.6357892\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a resource-allocating network based on local conditions (RAN-LC) is proposed to avoid the existing problems of RAN. This method gets the initial hidden nodes by using K-means clustering algorithm and the characteristics of activation function, and it utilizes new Novelty Criterion based on local conditions instead of the old one to keep the network neat and efficient. Moreover, it adopts Multi-patterns to enhance the generalization ability of network in the state of parameters adjustment. The simulation results show that this method can generate network quickly and more reasonable. The network generated finally has good performance and also works well in the prediction of nonlinear systems.\",\"PeriodicalId\":114901,\"journal\":{\"name\":\"Proceedings of the 10th World Congress on Intelligent Control and Automation\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 10th World Congress on Intelligent Control and Automation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WCICA.2012.6357892\",\"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 10th World Congress on Intelligent Control and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WCICA.2012.6357892","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A resource-allocating network based on local conditions and its application in prediction of nonlinear systems
In this paper, a resource-allocating network based on local conditions (RAN-LC) is proposed to avoid the existing problems of RAN. This method gets the initial hidden nodes by using K-means clustering algorithm and the characteristics of activation function, and it utilizes new Novelty Criterion based on local conditions instead of the old one to keep the network neat and efficient. Moreover, it adopts Multi-patterns to enhance the generalization ability of network in the state of parameters adjustment. The simulation results show that this method can generate network quickly and more reasonable. The network generated finally has good performance and also works well in the prediction of nonlinear systems.