LINEAR NEURAL NETWORK TRAINING ALGORITHMS FOR REAL-WORLD BENCHMARK PROBLEMS.

This paper describes the Adaptive Steepest Descent (ASD) and Optimal Fletcher-Reeves (OFR) algorithms for linear neural network training. The algorithms are applied to well-known pattern classification and function approximation problems, belonging to benchmark collection Proben1. The paper discusses the convergence behavior and performance of the ASD and OFR training algorithms by computer simulations and compares the results with those produced by linear-RPROP method. [ABSTRACT FROM AUTHOR]