Feed-forward network training using optimal input gains
Sanjeev S. Malalur, MICHAEL T. MANRY · 2009
In this paper, an effective batch training algorithm is developed for feed-forward networks such as the multilayer perceptron. First, the effects of input transforms are reviewed and explained, using the concept of equivalent networks. Next, a non-singular diagonal transform matrix for the inputs is proposed. Use of this transform is equivalent to altering the input gains in the network. Newton's method is used to solve for the input gains and an optimal learning factor. In several examples, it is shown that the final algorithm is a reasonable compromise between first order training methods and Levenburg-Marquardt.