基于模糊神经网络的水浴系统温度控制

B. Bhushan, Ajit Kumar Sharma, Deepti Singh
{"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":"92 1","pages":"0"},"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\":\"92 1\",\"pages\":\"0\"},\"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}
引用次数: 1

摘要

传统的控制器通常需要事先了解过程的数学建模。数学建模的不准确性降低了过程的性能,特别是对于非线性和复杂的控制问题。为了克服上述困难,实现了模糊逻辑(FL)和自适应神经模糊推理系统(ANFIS)等智能控制器。模糊控制器被设计成与语言控制规则形式的知识一起工作。但是,将这些语言规则转化为模糊集理论的框架取决于某些参数的选择,而这些参数的选择尚无形式化方法。分析表明,ANFIS最适合于上述系统的自适应温度控制。与FLC相比,ANFIS产生稳定的控制信号。它具有更好的温度跟踪能力,几乎为零超调和最小的绝对误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信