{"title":"感应电机驱动应用中的软计算技术综述","authors":"Gadwala Durgasukumar, Repana Ramanjan Prasad, Srinivasa Rao Gorantla","doi":"10.11591/ijpeds.v15.i2.pp753-768","DOIUrl":null,"url":null,"abstract":"<div align=\"center\"><table width=\"590\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\"><tbody><tr><td valign=\"top\" width=\"387\"><p>In this paper, hybrid models based on fuzzy systems and neural networks are reviewed. A fuzzy inference system is explicitly represented by expertise for induction motor drives, incorporating the learning capability of artificial neural networks. Researchers have been attracted to neuro-fuzzy techniques for training and inference in induction motor drives due to their efficiency. According to the classification of research articles from 2000 to 2020, this article presents a review of different artificial neural network techniques, fuzzy and neuro-fuzzy systems. The main objective is to provide a concise overview of current neuro-fuzzy research and to enable readers to identify appropriate methods according to their research interests.</p><p> </p></td></tr></tbody></table></div>","PeriodicalId":355274,"journal":{"name":"International Journal of Power Electronics and Drive Systems (IJPEDS)","volume":"3 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A review on soft computing techniques used in induction motor drive application\",\"authors\":\"Gadwala Durgasukumar, Repana Ramanjan Prasad, Srinivasa Rao Gorantla\",\"doi\":\"10.11591/ijpeds.v15.i2.pp753-768\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div align=\\\"center\\\"><table width=\\\"590\\\" border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\"><tbody><tr><td valign=\\\"top\\\" width=\\\"387\\\"><p>In this paper, hybrid models based on fuzzy systems and neural networks are reviewed. A fuzzy inference system is explicitly represented by expertise for induction motor drives, incorporating the learning capability of artificial neural networks. Researchers have been attracted to neuro-fuzzy techniques for training and inference in induction motor drives due to their efficiency. According to the classification of research articles from 2000 to 2020, this article presents a review of different artificial neural network techniques, fuzzy and neuro-fuzzy systems. The main objective is to provide a concise overview of current neuro-fuzzy research and to enable readers to identify appropriate methods according to their research interests.</p><p> </p></td></tr></tbody></table></div>\",\"PeriodicalId\":355274,\"journal\":{\"name\":\"International Journal of Power Electronics and Drive Systems (IJPEDS)\",\"volume\":\"3 2\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Power Electronics and Drive Systems (IJPEDS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.11591/ijpeds.v15.i2.pp753-768\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Power Electronics and Drive Systems (IJPEDS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.11591/ijpeds.v15.i2.pp753-768","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A review on soft computing techniques used in induction motor drive application
In this paper, hybrid models based on fuzzy systems and neural networks are reviewed. A fuzzy inference system is explicitly represented by expertise for induction motor drives, incorporating the learning capability of artificial neural networks. Researchers have been attracted to neuro-fuzzy techniques for training and inference in induction motor drives due to their efficiency. According to the classification of research articles from 2000 to 2020, this article presents a review of different artificial neural network techniques, fuzzy and neuro-fuzzy systems. The main objective is to provide a concise overview of current neuro-fuzzy research and to enable readers to identify appropriate methods according to their research interests.