{"title":"前馈神经网络训练改进的基于对立的粒子群算法","authors":"M. Rashid, A. R. Baig","doi":"10.1109/ICISA.2010.5480380","DOIUrl":null,"url":null,"abstract":"In this study we present an improved opposition- based PSO and apply it to feedforward neural network training. The improved opposition-based PSO utilizes opposition-based initialization, opposition-based generation jumping and opposition-based velocity calculation. The opposition-based PSO is first tested on some unimodal and multimodal problems and its performance is compared with standard PSO. We then test the performance of the improved opposition-based PSO for training feedforward neural network and also present a comparison with standard PSO.","PeriodicalId":313762,"journal":{"name":"2010 International Conference on Information Science and Applications","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"22","resultStr":"{\"title\":\"Improved Opposition-Based PSO for Feedforward Neural Network Training\",\"authors\":\"M. Rashid, A. R. Baig\",\"doi\":\"10.1109/ICISA.2010.5480380\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study we present an improved opposition- based PSO and apply it to feedforward neural network training. The improved opposition-based PSO utilizes opposition-based initialization, opposition-based generation jumping and opposition-based velocity calculation. The opposition-based PSO is first tested on some unimodal and multimodal problems and its performance is compared with standard PSO. We then test the performance of the improved opposition-based PSO for training feedforward neural network and also present a comparison with standard PSO.\",\"PeriodicalId\":313762,\"journal\":{\"name\":\"2010 International Conference on Information Science and Applications\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-04-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"22\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Information Science and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICISA.2010.5480380\",\"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 International Conference on Information Science and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICISA.2010.5480380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved Opposition-Based PSO for Feedforward Neural Network Training
In this study we present an improved opposition- based PSO and apply it to feedforward neural network training. The improved opposition-based PSO utilizes opposition-based initialization, opposition-based generation jumping and opposition-based velocity calculation. The opposition-based PSO is first tested on some unimodal and multimodal problems and its performance is compared with standard PSO. We then test the performance of the improved opposition-based PSO for training feedforward neural network and also present a comparison with standard PSO.