{"title":"基于粒子群优化的神经网络研究","authors":"Yahui Wang, Zhifeng Xia, Yifeng Huo","doi":"10.1109/ICICIS.2011.106","DOIUrl":null,"url":null,"abstract":"In view of the artificial neural network weights training problem, this paper proposed a method to optimize the network's structure parameters and regularization coefficient using two-layer Particle Swarm Optimization (PSO). This algorithm was applied to train Adaline network. Compared with fixed regularization coefficient method and Sliding Mode Variable Structure optimization method, the result showed that it had the advantages of high precision and strong ability of generalization.","PeriodicalId":255291,"journal":{"name":"2011 International Conference on Internet Computing and Information Services","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Neural Network Research Using Particle Swarm Optimization\",\"authors\":\"Yahui Wang, Zhifeng Xia, Yifeng Huo\",\"doi\":\"10.1109/ICICIS.2011.106\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In view of the artificial neural network weights training problem, this paper proposed a method to optimize the network's structure parameters and regularization coefficient using two-layer Particle Swarm Optimization (PSO). This algorithm was applied to train Adaline network. Compared with fixed regularization coefficient method and Sliding Mode Variable Structure optimization method, the result showed that it had the advantages of high precision and strong ability of generalization.\",\"PeriodicalId\":255291,\"journal\":{\"name\":\"2011 International Conference on Internet Computing and Information Services\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Internet Computing and Information Services\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICICIS.2011.106\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Internet Computing and Information Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICIS.2011.106","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural Network Research Using Particle Swarm Optimization
In view of the artificial neural network weights training problem, this paper proposed a method to optimize the network's structure parameters and regularization coefficient using two-layer Particle Swarm Optimization (PSO). This algorithm was applied to train Adaline network. Compared with fixed regularization coefficient method and Sliding Mode Variable Structure optimization method, the result showed that it had the advantages of high precision and strong ability of generalization.
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