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- W2384205518 abstract "In order to solve the problem of the failure of measure parameters and online optimal running in generator units,a novel parameter prediction and estimation method based on kernel partial least squares(KPLS) was proposed.The prediction and estimation mathematic model was established firstly and online estimation of parameters was performed.The basic idea was first mapped data from original space into feature space and then performs PLS regression in the feature space.The proposed method can effectively capture the nonlinear relationship among process variables and has better estimation performance than PLS and other linear approaches.Simulation results of some 1 000 MW turbine generator's data prove that the method is effective." @default.
- W2384205518 created "2016-06-24" @default.
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- W2384205518 date "2011-01-01" @default.
- W2384205518 modified "2023-09-22" @default.
- W2384205518 title "Parameter Prediction and Estimation of Turbine Generator Based on Kernel Partial Least Squares" @default.
- W2384205518 hasPublicationYear "2011" @default.
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