{"title":"基于模糊神经网络的水浴系统温度控制","authors":"B. Bhushan, Ajit Kumar Sharma, Deepti Singh","doi":"10.1109/ICPEICES.2016.7853729","DOIUrl":null,"url":null,"abstract":"Conventional controllers usually require a prior knowledge of mathematical modeling of the process. The inaccuracy of mathematical modeling degrades the performance of the process, especially for non-linear and complex control problem. To overcome above difficulties intelligent controllers like Fuzzy Logic (FL) and Adaptive Neuro-Fuzzy Inference System (ANFIS), are implemented. The Fuzzy controller is designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. It is analyzed that ANFIS is best suitable for adaptive temperature control of above system. As compared to FLC, ANFIS produces a stable control signal. It has much better temperature tracking capability with almost zero overshoot and minimum absolute error.","PeriodicalId":305942,"journal":{"name":"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Fuzzy & ANFIS based temperature control of water bath system\",\"authors\":\"B. Bhushan, Ajit Kumar Sharma, Deepti Singh\",\"doi\":\"10.1109/ICPEICES.2016.7853729\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Conventional controllers usually require a prior knowledge of mathematical modeling of the process. The inaccuracy of mathematical modeling degrades the performance of the process, especially for non-linear and complex control problem. To overcome above difficulties intelligent controllers like Fuzzy Logic (FL) and Adaptive Neuro-Fuzzy Inference System (ANFIS), are implemented. The Fuzzy controller is designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. It is analyzed that ANFIS is best suitable for adaptive temperature control of above system. As compared to FLC, ANFIS produces a stable control signal. It has much better temperature tracking capability with almost zero overshoot and minimum absolute error.\",\"PeriodicalId\":305942,\"journal\":{\"name\":\"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPEICES.2016.7853729\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPEICES.2016.7853729","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy & ANFIS based temperature control of water bath system
Conventional controllers usually require a prior knowledge of mathematical modeling of the process. The inaccuracy of mathematical modeling degrades the performance of the process, especially for non-linear and complex control problem. To overcome above difficulties intelligent controllers like Fuzzy Logic (FL) and Adaptive Neuro-Fuzzy Inference System (ANFIS), are implemented. The Fuzzy controller is designed to work with knowledge in the form of linguistic control rules. But the translation of these linguistic rules into the framework of fuzzy set theory depends on the choice of certain parameters, for which no formal method is known. It is analyzed that ANFIS is best suitable for adaptive temperature control of above system. As compared to FLC, ANFIS produces a stable control signal. It has much better temperature tracking capability with almost zero overshoot and minimum absolute error.