文件名称:PVSYSTEMprodict
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随机权神经网络预测光伏电池的光功率的matlab仿真源代码-A random vector functional link network (RVFLN)It is proved that the RVFLN not only trains much faster with
good generalization performance, but also overcomes some pos- sible fl aws (such as the low convergence rate and the local
minimum) of the BP algorithm. But poor quality RVFLN is usually obtained whenever the training data are corrupted with outliers.
good generalization performance, but also overcomes some pos- sible fl aws (such as the low convergence rate and the local
minimum) of the BP algorithm. But poor quality RVFLN is usually obtained whenever the training data are corrupted with outliers.
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下载文件列表
PVSYSTEMprodict/AFTERregressionmethod.m
PVSYSTEMprodict/AFTERregressionmethod.txt
PVSYSTEMprodict/AFTERregressionmethodLXUANZE.m
PVSYSTEMprodict/afterregressionSHUCHUJIEGUO.mat
PVSYSTEMprodict/afterregressiontest.fig
PVSYSTEMprodict/afterregressiontrainset.fig
PVSYSTEMprodict/ANTIGYH.m
PVSYSTEMprodict/art%3A10.1007%2Fs10489-013-0501-1.pdf
PVSYSTEMprodict/Beforeregression.txt
PVSYSTEMprodict/BeforeregressionSHUCHUJIEGUO.mat
PVSYSTEMprodict/beforeregressiontest.fig
PVSYSTEMprodict/beforeregressiontestANTI.fig
PVSYSTEMprodict/beforeregressiontrain.fig
PVSYSTEMprodict/beforeregressiontrainANTI.fig
PVSYSTEMprodict/DATA.mat
PVSYSTEMprodict/FENLEI.mat
PVSYSTEMprodict/GYH.m
PVSYSTEMprodict/GYH.mat
PVSYSTEMprodict/H.mat
PVSYSTEMprodict/Hiddenlayersselect.fig
PVSYSTEMprodict/L.asv
PVSYSTEMprodict/L.m
PVSYSTEMprodict/lamuda.mat
PVSYSTEMprodict/Lamudaselect.mat
PVSYSTEMprodict/LQ.asv
PVSYSTEMprodict/LQ2jieguo.m
PVSYSTEMprodict/QLxunhuan.m
PVSYSTEMprodict/regressionmethod.m
PVSYSTEMprodict/regressionmethodlamudaselect.m
PVSYSTEMprodict/selectlamuda.fig
PVSYSTEMprodict/SHUCHUJG.mat
PVSYSTEMprodict/SHUCHUJGre.mat
PVSYSTEMprodict/SHURU.mat
PVSYSTEMprodict/sumLQ.mat
PVSYSTEMprodict/T.mat
PVSYSTEMprodict/TV.T
PVSYSTEMprodict
PVSYSTEMprodict/AFTERregressionmethod.txt
PVSYSTEMprodict/AFTERregressionmethodLXUANZE.m
PVSYSTEMprodict/afterregressionSHUCHUJIEGUO.mat
PVSYSTEMprodict/afterregressiontest.fig
PVSYSTEMprodict/afterregressiontrainset.fig
PVSYSTEMprodict/ANTIGYH.m
PVSYSTEMprodict/art%3A10.1007%2Fs10489-013-0501-1.pdf
PVSYSTEMprodict/Beforeregression.txt
PVSYSTEMprodict/BeforeregressionSHUCHUJIEGUO.mat
PVSYSTEMprodict/beforeregressiontest.fig
PVSYSTEMprodict/beforeregressiontestANTI.fig
PVSYSTEMprodict/beforeregressiontrain.fig
PVSYSTEMprodict/beforeregressiontrainANTI.fig
PVSYSTEMprodict/DATA.mat
PVSYSTEMprodict/FENLEI.mat
PVSYSTEMprodict/GYH.m
PVSYSTEMprodict/GYH.mat
PVSYSTEMprodict/H.mat
PVSYSTEMprodict/Hiddenlayersselect.fig
PVSYSTEMprodict/L.asv
PVSYSTEMprodict/L.m
PVSYSTEMprodict/lamuda.mat
PVSYSTEMprodict/Lamudaselect.mat
PVSYSTEMprodict/LQ.asv
PVSYSTEMprodict/LQ2jieguo.m
PVSYSTEMprodict/QLxunhuan.m
PVSYSTEMprodict/regressionmethod.m
PVSYSTEMprodict/regressionmethodlamudaselect.m
PVSYSTEMprodict/selectlamuda.fig
PVSYSTEMprodict/SHUCHUJG.mat
PVSYSTEMprodict/SHUCHUJGre.mat
PVSYSTEMprodict/SHURU.mat
PVSYSTEMprodict/sumLQ.mat
PVSYSTEMprodict/T.mat
PVSYSTEMprodict/TV.T
PVSYSTEMprodict
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