{"title":"神经模糊控制系统的实现","authors":"Sergey M. Morozov","doi":"10.1109/CTS53513.2021.9562822","DOIUrl":null,"url":null,"abstract":"Neuro-fuzzy approach for implementing control systems is considered. Neuro-fuzzy systems are a tool for a development of trainable control systems with high interpretability. These systems can be trained to work in new conditions. There is a possibility to analyze the actions, which implement the control. Examples of neuro-fuzzy control applications are presented: virtual assistant and automatic calibration system.","PeriodicalId":371882,"journal":{"name":"2021 IV International Conference on Control in Technical Systems (CTS)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Implementation of Neuro-fuzzy Control Systems\",\"authors\":\"Sergey M. Morozov\",\"doi\":\"10.1109/CTS53513.2021.9562822\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Neuro-fuzzy approach for implementing control systems is considered. Neuro-fuzzy systems are a tool for a development of trainable control systems with high interpretability. These systems can be trained to work in new conditions. There is a possibility to analyze the actions, which implement the control. Examples of neuro-fuzzy control applications are presented: virtual assistant and automatic calibration system.\",\"PeriodicalId\":371882,\"journal\":{\"name\":\"2021 IV International Conference on Control in Technical Systems (CTS)\",\"volume\":\"151 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IV International Conference on Control in Technical Systems (CTS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CTS53513.2021.9562822\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IV International Conference on Control in Technical Systems (CTS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CTS53513.2021.9562822","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neuro-fuzzy approach for implementing control systems is considered. Neuro-fuzzy systems are a tool for a development of trainable control systems with high interpretability. These systems can be trained to work in new conditions. There is a possibility to analyze the actions, which implement the control. Examples of neuro-fuzzy control applications are presented: virtual assistant and automatic calibration system.
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