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《Journal of Dalian University of Technology》 2007-01
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Identification procedure of vibrating load parameters of hydraulic generator with RBF neural network

LI Shou-ju~(*1),LIU Ying-xi~1,SONG Shu-chuan~2,FENG Yan-rong~2 (1.State Key Lab.of Struct.Anal.for Ind.Equip.,Dalian Univ.of Technol.,Dalian 116024,China;2.Fengman Hydropower Plant,Jilin 132108,China)  
Vibrating dynamic characteristics have been unknown but important in the modeling and mechanical analyses of large hydraulic generators.An identification algorithm for vibrating dynamic characterization by using RBF(radial basis function) artificial neural network is developed for multi-degree of freedom systems.By means of measured dynamic responses of the hydraulic generator at Fengman Hydropower Plant,the indentification algorithm identifies the loading parameters which include the main frequencies,phase differences and amplitudes of vibrating forces.The artificial neural network is used to tackle an ill-posed problem of the parameter identification and to approximate nonlinear function relationship between the vibration responses of the hydraulic generator and model parameters. It is demonstrated that a well-trained artificial neural network reveals an extremely fast convergence and a high degree of accuracy in the parameter identification of hydraulic generator vibration.
【Fund】: 国家自然科学基金资助项目(10472025)
【CateGory Index】: TV734.1
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