{"title":"Design of fuzzy controllers for semi-active suspension generated through the genetic algorithm","authors":"T. Hashiyama, T. Furuhashi, Y. Uchikawa","doi":"10.1109/ANNES.1995.499464","DOIUrl":null,"url":null,"abstract":"Presents a new method to generate fuzzy controllers through the use of a genetic algorithm (GA). Genetic operations are implemented to determine almost all of the parameters in the fuzzy controllers, such as the input variables, membership functions and fuzzy control rules. These parameters are encoded into the chromosomes. Setting the performance index is the only procedure necessary for the designer of the controllers. A GA with a new local improvement mechanism, which is based on genetic recombination in bacterial genetics, is applied to our method. Comparisons with other conventional GAs are also discussed. To show the effectiveness of our approach, fuzzy controllers for a semi-active suspension system are generated.","PeriodicalId":123427,"journal":{"name":"Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1995-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 1995 Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANNES.1995.499464","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Presents a new method to generate fuzzy controllers through the use of a genetic algorithm (GA). Genetic operations are implemented to determine almost all of the parameters in the fuzzy controllers, such as the input variables, membership functions and fuzzy control rules. These parameters are encoded into the chromosomes. Setting the performance index is the only procedure necessary for the designer of the controllers. A GA with a new local improvement mechanism, which is based on genetic recombination in bacterial genetics, is applied to our method. Comparisons with other conventional GAs are also discussed. To show the effectiveness of our approach, fuzzy controllers for a semi-active suspension system are generated.
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