Training neural network with damped oscillation and maximized gradient function

Constant learning rate (LR) which is most widely used for training neural networks (NNs) in back propagation (BP) but it is not usually preferable due to its slow convergence rate while using small learning rate and it also shows less accuracy while using higher learning rate. In this paper, we are proposing a faster and supervised algorithm which shows more accuracy in a few iterations while dealing with neural networks (NNs). Training of NNs with damped oscillation and maximized gradient function (DOMG) deals with the implementation of damped oscillation in learning rate called damped learning rate (DLR) by which we get more accuracy in a few iterations and maximized gradient function is used for fast weight updating. DOMG is significantly tested on eight real world benchmark classification problems such as heart disease, ionosphere, Australian credit card, time series, wine, horse, glass and soybean identification. The proposed DOMG outperforms the existing BP in terms of convergence rate and generalization ability.