{"title":"基于经典神经网络和动态神经网络的非线性生物反应器装置辨识与控制","authors":"M. Onder Efe, O. Kaynak","doi":"10.1109/ISIE.1997.648914","DOIUrl":null,"url":null,"abstract":"In this study, the identification and control of a bioreactor plant using neural networks is considered with three different control strategies, namely, inverse control strategy, self-learning control and dynamical neural units for control of nonlinear dynamical systems. The performance of these methods are compared using several comparison measures.","PeriodicalId":134474,"journal":{"name":"ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics","volume":"171 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1997-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Identification and control of a nonlinear bioreactor plant using classical and dynamical neural networks\",\"authors\":\"M. Onder Efe, O. Kaynak\",\"doi\":\"10.1109/ISIE.1997.648914\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, the identification and control of a bioreactor plant using neural networks is considered with three different control strategies, namely, inverse control strategy, self-learning control and dynamical neural units for control of nonlinear dynamical systems. The performance of these methods are compared using several comparison measures.\",\"PeriodicalId\":134474,\"journal\":{\"name\":\"ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics\",\"volume\":\"171 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1997-07-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISIE.1997.648914\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ISIE '97 Proceeding of the IEEE International Symposium on Industrial Electronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISIE.1997.648914","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification and control of a nonlinear bioreactor plant using classical and dynamical neural networks
In this study, the identification and control of a bioreactor plant using neural networks is considered with three different control strategies, namely, inverse control strategy, self-learning control and dynamical neural units for control of nonlinear dynamical systems. The performance of these methods are compared using several comparison measures.