{"title":"基于前馈神经网络的化工反应器基准并行自适应控制","authors":"D. Cajueiro, E. M. Hemerly","doi":"10.1109/SBRN.2000.889711","DOIUrl":null,"url":null,"abstract":"This paper applies a parallel scheme for adaptive control that uses only one neural network to a CSTR (continuous stirred tank reactor). Convergence of the identification error is investigated by Lyapunov's second method. The training process of the neural network is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm.","PeriodicalId":448461,"journal":{"name":"Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2000-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A chemical reactor benchmark for parallel adaptive control using feedforward neural networks\",\"authors\":\"D. Cajueiro, E. M. Hemerly\",\"doi\":\"10.1109/SBRN.2000.889711\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper applies a parallel scheme for adaptive control that uses only one neural network to a CSTR (continuous stirred tank reactor). Convergence of the identification error is investigated by Lyapunov's second method. The training process of the neural network is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm.\",\"PeriodicalId\":448461,\"journal\":{\"name\":\"Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2000-01-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SBRN.2000.889711\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. Vol.1. Sixth Brazilian Symposium on Neural Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SBRN.2000.889711","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A chemical reactor benchmark for parallel adaptive control using feedforward neural networks
This paper applies a parallel scheme for adaptive control that uses only one neural network to a CSTR (continuous stirred tank reactor). Convergence of the identification error is investigated by Lyapunov's second method. The training process of the neural network is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm.
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