{"title":"基于pso的权重学习技术的加权模糊插值推理新方法","authors":"Shyi-Ming Chen, Wen-Chyuan Hsin, Yu-Chuan Chang","doi":"10.1109/ICMLC.2012.6359579","DOIUrl":null,"url":null,"abstract":"In this paper, we present a weighted fuzzy interpolative reasoning method based on the proposed PSO-based weights-learning algorithm. We also apply the proposed method to deal with the computer activity prediction problem. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the optimally learned weights obtained by the proposed PSO-based weights-learning algorithm gets smaller relative squared error rates than the existing methods.","PeriodicalId":128006,"journal":{"name":"2012 International Conference on Machine Learning and Cybernetics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A new method for weighted fuzzy interpolative reasoning based on PSO-based weights-learning techniques\",\"authors\":\"Shyi-Ming Chen, Wen-Chyuan Hsin, Yu-Chuan Chang\",\"doi\":\"10.1109/ICMLC.2012.6359579\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we present a weighted fuzzy interpolative reasoning method based on the proposed PSO-based weights-learning algorithm. We also apply the proposed method to deal with the computer activity prediction problem. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the optimally learned weights obtained by the proposed PSO-based weights-learning algorithm gets smaller relative squared error rates than the existing methods.\",\"PeriodicalId\":128006,\"journal\":{\"name\":\"2012 International Conference on Machine Learning and Cybernetics\",\"volume\":\"39 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2012.6359579\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2012.6359579","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new method for weighted fuzzy interpolative reasoning based on PSO-based weights-learning techniques
In this paper, we present a weighted fuzzy interpolative reasoning method based on the proposed PSO-based weights-learning algorithm. We also apply the proposed method to deal with the computer activity prediction problem. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the optimally learned weights obtained by the proposed PSO-based weights-learning algorithm gets smaller relative squared error rates than the existing methods.