Table 4: Performance of varying training algorithms of ANN model.

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