{"title":"基于神经网络的预测控制优化","authors":"Jean Saint Donat, N. Bhat, T. McAvoy","doi":"10.1109/ACC.1990.4174181","DOIUrl":null,"url":null,"abstract":"Neural networks hold great promise for application in the general area of process control. This paper focuses on using a backpropagation network in an optimization based model predictive control scheme. Since analytical expressions for the gradient and Hessian of the neural net model can be derived and these expressions can be calculated in paralle, extremely fast computation times are possible. The control approach is illustrated on a pH CSTR example.","PeriodicalId":307181,"journal":{"name":"1990 American Control Conference","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1990-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":"{\"title\":\"Optimizing Neural Net based Predictive Control\",\"authors\":\"Jean Saint Donat, N. Bhat, T. McAvoy\",\"doi\":\"10.1109/ACC.1990.4174181\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Neural networks hold great promise for application in the general area of process control. This paper focuses on using a backpropagation network in an optimization based model predictive control scheme. Since analytical expressions for the gradient and Hessian of the neural net model can be derived and these expressions can be calculated in paralle, extremely fast computation times are possible. The control approach is illustrated on a pH CSTR example.\",\"PeriodicalId\":307181,\"journal\":{\"name\":\"1990 American Control Conference\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1990-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"21\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"1990 American Control Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ACC.1990.4174181\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"1990 American Control Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ACC.1990.4174181","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Neural networks hold great promise for application in the general area of process control. This paper focuses on using a backpropagation network in an optimization based model predictive control scheme. Since analytical expressions for the gradient and Hessian of the neural net model can be derived and these expressions can be calculated in paralle, extremely fast computation times are possible. The control approach is illustrated on a pH CSTR example.
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