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主要題名:A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression
作者姓名:Wu, Chih-HungTzeng, Gwo-HshiungLin, Rong-Ho
貢獻者資料:管理學院/工業工程與管理系
關鍵詞:support vector regression (SVR)hybrid genetic algorithm (HGA)parameter optimizationkernel function optimizationelectrical load forecastingforecasting accuracy
論文中文摘要:This study developed a novel model, HGA-SVR, for type of kernel function and kernel parameter value
optimization in support vector regression (SVR), which is then applied to forecast the maximum electrical
daily load. A novel hybrid genetic algorithm (HGA) was adapted to search for the optimal type of kernel
function and kernel parameter values of SVR to increase the accuracy of SVR. The proposed model was
tested at an electricity load forecasting competition announced on the EUNITE network. The results
showed that the new HGA-SVR model outperforms the previous models. Specifically, the new HGASVR
model can successfully identify the optimal type of kernel function and all the optimal values of
the parameters of SVR with the lowest prediction error values in electricity load forecasting.
出版日期:2009-04
論文ID:2013090500246
數位物件檔名:10821-pm-pa-2009-04_2p.pdf
統一資源識別號:http://dx.doi.org/10.1016/j.eswa.2008.06.046
備註:© 2008 Published by Elsevier Ltd. All rights reserved.
資料開放狀態:開放
刊物名稱:Expert Systems with Applications
期數:36(3)
論文起迄頁碼:4725–4735