  The present work deals with an improved back-propagation algorithm based on
Gauss-Newton numerical optimization method for fast convergence. The steepest
descent method is used for the back-propagation. The algorithm is tested using
various datasets and compared with the steepest descent back-propagation
algorithm. In the system, optimization is carried out using multilayer neural
network. The efficacy of the proposed method is observed during the training
period as it converges quickly for the dataset used in test. The requirement of
memory for computing the steps of algorithm is also analyzed.
