{"title":"汽轮发电机组神经网络模型和控制器的在线训练","authors":"Qinghua Wu, B. Hogg, George W. Irwin","doi":"10.1109/ANN.1991.213515","DOIUrl":null,"url":null,"abstract":"The authors are concerned with the development of a neural network (NN) regulator for turbogenerator adaptive control. The NN regulator is designed based on a hierarchical architecture of neural networks. The back-propagation (BP) algorithm is used hierarchically in the NN regulator for on-line training of the turbogenerator NN model and controller. Dynamic modelling of the turbogenerator system has been investigated using the multilayer NN. The NN regulator has been implemented on a simulated complex nonlinear turbogenerator system. Simulation results evaluating the performance of the NN regulator under different operation conditions and disturbances are presented.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"205 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"On-line training of neural network model and controller for turbogenerators\",\"authors\":\"Qinghua Wu, B. Hogg, George W. Irwin\",\"doi\":\"10.1109/ANN.1991.213515\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors are concerned with the development of a neural network (NN) regulator for turbogenerator adaptive control. The NN regulator is designed based on a hierarchical architecture of neural networks. The back-propagation (BP) algorithm is used hierarchically in the NN regulator for on-line training of the turbogenerator NN model and controller. Dynamic modelling of the turbogenerator system has been investigated using the multilayer NN. The NN regulator has been implemented on a simulated complex nonlinear turbogenerator system. Simulation results evaluating the performance of the NN regulator under different operation conditions and disturbances are presented.<<ETX>>\",\"PeriodicalId\":119713,\"journal\":{\"name\":\"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems\",\"volume\":\"205 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ANN.1991.213515\",\"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 of the First International Forum on Applications of Neural Networks to Power Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ANN.1991.213515","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On-line training of neural network model and controller for turbogenerators
The authors are concerned with the development of a neural network (NN) regulator for turbogenerator adaptive control. The NN regulator is designed based on a hierarchical architecture of neural networks. The back-propagation (BP) algorithm is used hierarchically in the NN regulator for on-line training of the turbogenerator NN model and controller. Dynamic modelling of the turbogenerator system has been investigated using the multilayer NN. The NN regulator has been implemented on a simulated complex nonlinear turbogenerator system. Simulation results evaluating the performance of the NN regulator under different operation conditions and disturbances are presented.<>
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