Experiments in finding neural network weights

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Abstract
This report compares the speed with which back-propagation, conjugate gradient, and Quickprop algorithms find the neural network weights that solve: a small-scale nonlinear classification problem and a mapping from a vector-valued time series to a one-dimensional signal. The problem of tuning each algorithm for these problems (i.e., selecting a good set of control parameters) is also discussed. The most efficient algorithm was found to be an enhanced form of back-propagation using large momentum and learning rates, with weight updates after each pattern presentation and a small constant added to the error derivatives. 3 refs., 6 tabs.

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