{"title":"论利用神经网络控制加热器的方法","authors":"S. S. Abdurakipov, E. B. Butakov","doi":"10.3103/s8756699024700328","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The possibility of using a long short-term memory (LSTM) neural network has been studied to simulate the operation of a PID controller. A programmable PID controller has been realized to control a heater with a temperature sensor on the basis of an Arduino microcontroller. A LSTM model trained on the controller data has been developed. It is shown that the neural network model accurately reproduces the operation of the controller and can completely replace it under the condition of a much greater but sufficient data processing time. The applicability of this model as a detector of abnormal operation of the PID controller is shown.</p>","PeriodicalId":44919,"journal":{"name":"Optoelectronics Instrumentation and Data Processing","volume":"42 1","pages":""},"PeriodicalIF":0.5000,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On a Method of Controlling Heater by Using Neural Networks\",\"authors\":\"S. S. Abdurakipov, E. B. Butakov\",\"doi\":\"10.3103/s8756699024700328\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Abstract</h3><p>The possibility of using a long short-term memory (LSTM) neural network has been studied to simulate the operation of a PID controller. A programmable PID controller has been realized to control a heater with a temperature sensor on the basis of an Arduino microcontroller. A LSTM model trained on the controller data has been developed. It is shown that the neural network model accurately reproduces the operation of the controller and can completely replace it under the condition of a much greater but sufficient data processing time. The applicability of this model as a detector of abnormal operation of the PID controller is shown.</p>\",\"PeriodicalId\":44919,\"journal\":{\"name\":\"Optoelectronics Instrumentation and Data Processing\",\"volume\":\"42 1\",\"pages\":\"\"},\"PeriodicalIF\":0.5000,\"publicationDate\":\"2024-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Optoelectronics Instrumentation and Data Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3103/s8756699024700328\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"PHYSICS, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Optoelectronics Instrumentation and Data Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s8756699024700328","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
On a Method of Controlling Heater by Using Neural Networks
Abstract
The possibility of using a long short-term memory (LSTM) neural network has been studied to simulate the operation of a PID controller. A programmable PID controller has been realized to control a heater with a temperature sensor on the basis of an Arduino microcontroller. A LSTM model trained on the controller data has been developed. It is shown that the neural network model accurately reproduces the operation of the controller and can completely replace it under the condition of a much greater but sufficient data processing time. The applicability of this model as a detector of abnormal operation of the PID controller is shown.
期刊介绍:
The scope of Optoelectronics, Instrumentation and Data Processing encompasses, but is not restricted to, the following areas: analysis and synthesis of signals and images; artificial intelligence methods; automated measurement systems; physicotechnical foundations of micro- and optoelectronics; optical information technologies; systems and components; modelling in physicotechnical research; laser physics applications; computer networks and data transmission systems. The journal publishes original papers, reviews, and short communications in order to provide the widest possible coverage of latest research and development in its chosen field.