R. P. Landim, B. R. Menezes, S. Silva, W. Caminhas
{"title":"在线新模糊神经元状态观测器","authors":"R. P. Landim, B. R. Menezes, S. Silva, W. Caminhas","doi":"10.1109/SBRN.2000.889738","DOIUrl":null,"url":null,"abstract":"An algorithm for a state observation based on a neo-fuzzy-neuron (NFN) with real time training is presented. Some useful theorems are promptly demonstrated and used to aid the design of the observer. Two applications of this state observer are shown: an induction machine rotor flux observer and an induction machine speed observer. Digital simulation and experimental results show the good performance of the observer.","PeriodicalId":448461,"journal":{"name":"Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Online neo-fuzzy-neuron state observer\",\"authors\":\"R. P. Landim, B. R. Menezes, S. Silva, W. Caminhas\",\"doi\":\"10.1109/SBRN.2000.889738\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"An algorithm for a state observation based on a neo-fuzzy-neuron (NFN) with real time training is presented. Some useful theorems are promptly demonstrated and used to aid the design of the observer. Two applications of this state observer are shown: an induction machine rotor flux observer and an induction machine speed observer. Digital simulation and experimental results show the good performance of the observer.\",\"PeriodicalId\":448461,\"journal\":{\"name\":\"Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SBRN.2000.889738\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBRN.2000.889738","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An algorithm for a state observation based on a neo-fuzzy-neuron (NFN) with real time training is presented. Some useful theorems are promptly demonstrated and used to aid the design of the observer. Two applications of this state observer are shown: an induction machine rotor flux observer and an induction machine speed observer. Digital simulation and experimental results show the good performance of the observer.
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