Improved convergence for output scaling of a feedforward network with linear output nodes

The author presents an augmentation to the gradient descent learning algorithm for a feedforward neural network to improve the convergence of learning a desired output having an absolute value magnitude greater than one. The enhancement to the standard backpropagation algorithms is simple to implement in existing code, computationally efficient, and reduces the number of training cycles. With these features, this algorithm saves time in training a network that requires output scaling