{"title":"动态系统优化的模糊多目标适应度函数","authors":"X. Fang, B. Kellog, T. Conlan, J. Dickerson","doi":"10.1109/NAFIPS.2003.1226809","DOIUrl":null,"url":null,"abstract":"Genetic algorithms (GA) can be used to design complex multi-objective systems. The fitness function in the GA was designed using fuzzy multi-objective integer programming to capture fuzzy design goals such as driver comfort and quick stopping ability. The effectiveness of methods is demonstrated by a brake control design example.","PeriodicalId":153530,"journal":{"name":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","volume":"100 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fuzzy multi-objective fitness functions for dynamical system optimization\",\"authors\":\"X. Fang, B. Kellog, T. Conlan, J. Dickerson\",\"doi\":\"10.1109/NAFIPS.2003.1226809\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Genetic algorithms (GA) can be used to design complex multi-objective systems. The fitness function in the GA was designed using fuzzy multi-objective integer programming to capture fuzzy design goals such as driver comfort and quick stopping ability. The effectiveness of methods is demonstrated by a brake control design example.\",\"PeriodicalId\":153530,\"journal\":{\"name\":\"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003\",\"volume\":\"100 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NAFIPS.2003.1226809\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NAFIPS.2003.1226809","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy multi-objective fitness functions for dynamical system optimization
Genetic algorithms (GA) can be used to design complex multi-objective systems. The fitness function in the GA was designed using fuzzy multi-objective integer programming to capture fuzzy design goals such as driver comfort and quick stopping ability. The effectiveness of methods is demonstrated by a brake control design example.
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