{"title":"用模拟退火对模糊系统进行整定以预测有附加噪声的时间序列","authors":"Majid Almaraashi, R. John","doi":"10.1109/UKCI.2010.5625596","DOIUrl":null,"url":null,"abstract":"In this paper, a combination of fuzzy system models and simulated annealing are used to predict Mackey-Glass time series with different levels of added noise by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules under singleton and non-singleton fuzzifications for both Mamdani and Takagi-Sugeno (TSK). The results of the proposed methods are compared by their ability to handle uncertainty.","PeriodicalId":403291,"journal":{"name":"2010 UK Workshop on Computational Intelligence (UKCI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Tuning fuzzy systems by simulated annealing to predict time series with added noise\",\"authors\":\"Majid Almaraashi, R. John\",\"doi\":\"10.1109/UKCI.2010.5625596\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a combination of fuzzy system models and simulated annealing are used to predict Mackey-Glass time series with different levels of added noise by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules under singleton and non-singleton fuzzifications for both Mamdani and Takagi-Sugeno (TSK). The results of the proposed methods are compared by their ability to handle uncertainty.\",\"PeriodicalId\":403291,\"journal\":{\"name\":\"2010 UK Workshop on Computational Intelligence (UKCI)\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-11-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 UK Workshop on Computational Intelligence (UKCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UKCI.2010.5625596\",\"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 UK Workshop on Computational Intelligence (UKCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UKCI.2010.5625596","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Tuning fuzzy systems by simulated annealing to predict time series with added noise
In this paper, a combination of fuzzy system models and simulated annealing are used to predict Mackey-Glass time series with different levels of added noise by searching for the best configuration of the fuzzy system. Simulated annealing is used to optimise the parameters of the antecedent and the consequent parts of the fuzzy system rules under singleton and non-singleton fuzzifications for both Mamdani and Takagi-Sugeno (TSK). The results of the proposed methods are compared by their ability to handle uncertainty.
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