Backpropagation (BP) Algorithms |
Function |
MSE |
IN |
R2 |
Best Linear Eq. |
BFGS quasi-Newton backpropagation |
trainbfg |
0.1098 |
2 |
0.7007 |
y=0.96x-0.51 |
Bayesian regularization BP |
trainbr |
0.0003 |
28 |
- |
- |
Powell–Beale conjugate gradient backpropagation |
traincgb |
0.0035 |
15 |
0.9853 |
y=1.0x-0.05 |
Fletcher–Reeves conjugate gradient backpropagation |
traincgf |
0.5197 |
69 |
0.8834 |
y=0.93x+0.17 |
Polak-Ribiere conjugate gradient BP |
traincgp |
0.0604 |
1 |
0.5004 |
y=0.98x+0.11 |
Gradient descent |
traingd |
0.0949 |
1000 |
0.9525 |
y=1.1x-0.13 |
Gradient descent with momentum |
traingdm |
0.1909 |
9 |
0.6791 |
y=0.84x-0.35 |
Gradient descent with adaptive learning rate |
traingda |
0.0020 |
39 |
0.8731 |
y=1.0x-0.077 |
Gradient descent with momentum & Adaptive Learning |
traingdx |
0.8794 |
24 |
0.7412 |
y=1.0x-0.03 |
Levenberg–Marquardt backpropagation |
trainlm |
0.0367 |
5 |
0.8626 |
y=1.1x-0.14 |
One step secant backpropagation |
trainoss |
0.2171 |
6 |
0.8542 |
y=0.8x+0.064 |
Random weight/Bias |
trainr |
0.1585 |
5 |
0.8044 |
y=0.82x+0.18 |
Resilient backpropagation |
trainrp |
0.0040 |
5 |
0.8991 |
y=0.81x-0.1 |
Scaled conjugate gradient backpropagation |
trainscg |
0.1957 |
2 |
0.8092 |
y=0.79x+0.24 |