A generalized backpropagation algorithm for faster convergence

The conventional backpropagation algorithm is basically a gradient-descent method, it has the problems of local minima and slow convergence. A new generalized backpropagation algorithm which can effectively speed up the convergence rate and reduce the chance of being trapped in local minima is introduced in this paper. The new backpropagation algorithm is to change the derivative of the activation function so as to magnify the backward propagated error signal, thus the convergence rate can be accelerated and the local minimum can be escaped