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2.  Experimental Results  ELM  Weighted ELM  Locally Weighted ELM  Problem 2

3.  All training data are randomly chosen  Targets are normalize -1 to 1  Features are normalize 0 to 1  Using RMSE criterion 3

4.  Sinc function:  X=-10:0.05:10  Train:351  Test:50  (hidden neuron, h, k) Original ELM (10) Weighted ELM (10,0.01) Locally Weighted ELM (10,1,20) 1.95E-19.41E-51.53E-4 4

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6.  Function:  X=-5:0.05:5  Train:151  Test:50  (hidden neuron, h, k) 6 Original ELM (10) Weighted ELM (10,0.01) Locally Weighted ELM (10,1,20) T2FNN 2.81E-11.39E-48.15E-41.3E-3

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8.  Function:  X1,x2,x3=-1:0.005:1  Train:351  Test:50  (hidden neuron, h, k) 8 Original ELM (10) Weighted ELM (10,0.01) Locally Weighted ELM (10,1,20) 1.41E-43.09E-62.61E-5

9.  Machine CPU  Feature:6  Train:100  Test:109  (hidden neuron, h, k) Original ELM (10) Weighted ELM (10,0.9) Locally Weighted ELM (10,1,40) 0.1113420.1034730.105663 9

10.  Auto Price  Feature:15,1 nominal,14 continuous  Train:80  Test:79  (hidden neuron, h, k) Original ELM (15) Weighted ELM (10,0.9) Locally Weighted ELM (10,0.9,50) 0.2012550.1895840.193568 10

11.  Cancer  Feature:32  Train:100  Test:94  (hidden neuron, h, k) Original ELM (10) Weighted ELM (3,0.9) Locally Weighted ELM (3,1,40) 0.5336560.5284150.532317 11

12. Input layer hidden layer output layer The weights between input layer and hidden layer and the biases of neurons in the hidden layer are randomly chosen. 12

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15.  Ex 15

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17.  Find the k nearest training data to testing data 17

18.  Paper 數據  Randomly weight and bias  The output of Nearest data  (feature selection…?) 18 -0.93182 0.31220 5 0.02930 9 0.06156 2 000 1-0.953520.25826 0.06062 1 0.06156 2 0 0.01923 1 0.00568 2 2-0.98056 0.39312 2 0.02204 4 0.030280 0.01923 1 0.00568 2 3-0.97946 0.21105 9 0.02930 9 0.04592 1 0 0.03846 2 0.02272 7 6-0.9493 0.20431 6 0.00601 2 0.09284 3 0 0.01923 1 0.03409 1