training results

2
Epoch needed to Reach SSE = 35 with Training Speed of 0.1 #Hidden Nodes Weights = 0.5 Weights = Random 3 242 12 5 352 22 15 242 12 20 352 22 25 516 24 Epoch needed to Reach SSE = 35 with Training Speed of 0.5 #Hidden Nodes Weights = 0.5 Weights = Random 3 82 6 5 115 6 15 Not reached and Over train 7 20 Not reached after 820 Epochs 16 25 877 29 R^2 for each output and the sum of R^2 Max # Epoch = 1000 Training Speed = 0.1 #Hidden Nodes Weights = 0.5 Weights = Random 3 -0.259 0.730 0.864 0.986 0.503 0.929 0.998 0.999 2.106 3.644 5 -0.250 0.524 0.864 0.996 0.501 0.955 0.998 0.999 2.113 3.474 15 -0.037 0.319 0.874 0.999 0.529 0.964 0.998 0.999 2.364 3.281 20 0.092 0.217 0.880 0.999 0.557 0.968 0.998 0.999 2.527 3.183 25 0.200 0.199

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Training results of my neural network trained with the back-propagation algorithm

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Epoch needed to Reach SSE = 35 with Training Speed of 0.1

#Hidden NodesWeights = 0.5Weights = Random

324212

535222

1524212

2035222

2551624

Epoch needed to Reach SSE = 35 with Training Speed of 0.5

#Hidden NodesWeights = 0.5Weights = Random

3826

51156

15Not reached and Over train7

20Not reached after 820 Epochs16

2587729

R^2 for each output and the sum of R^2 Max # Epoch = 1000 Training Speed = 0.1

#Hidden NodesWeights = 0.5Weights = Random

3-0.2590.730

0.8640.986

0.5030.929

0.9980.999

2.1063.644

5-0.2500.524

0.8640.996

0.5010.955

0.9980.999

2.1133.474

15-0.0370.319

0.8740.999

0.5290.964

0.9980.999

2.3643.281

200.0920.217

0.8800.999

0.5570.968

0.9980.999

2.5273.183

250.2000.199

0.8850.999

0.5950.983

0.9980.999

2.6783.180

R^2 for each output and the sum of R^2 Max # Epoch = 1000 Training Speed = 0.3

#Hidden NodesWeights = 0.5Weights = Random

30.2300.785

0.8600.976

0.5150.702

0.9980.999

2.6033.462

5-0.2890.313

0.8610.999

0.5120.944

0.9980.999

2.0823.255

15-0.0810.345

0.8730.999

0.5480.969

0.9980.999

2.3383.312

200.0440.289

0.8790.998

0.5880.969

0.9880.999

2.4993.255

250.1410.325

0.8830.999

0.6440.964

0.9980.999

2.6163.287