Improved Time Training with Accuracy of Batch Back Propagation Algorithm Via Dynamic Learning Rate and Dynamic Momentum Factor

The man problem of batch back propagation (BBP) algorithm is slow training and there are several parameters needs to be adjusted manually, also suffers from saturation training. The learning rate and momentum factor are significant parameters for increasing the efficiency of the (BBP). In this study, we created a new dynamic function of each learning rate and momentum facor. We present the DBBPLM algorithm, which trains with a dynamic function for each the learning rate and momentum factor. A Sigmoid function used as activation function. The XOR problem, balance, breast cancer and iris dataset were used as benchmarks for testing the effects of the dynamic DBBPLM algorithm. All the experiments were performed on Matlab 2012 a. The stop training was determined ten power -5. From the experimental results, the DBBPLM algorithm provides superior performance in terms of training, and faster training with higher accuracy compared to the BBP algorithm and with existing works.