Using Quasirandom Numbers in Neural Networks

Peter G. Anderson

Department of Computer Science, RIT

Abstract

We present a novel training algorithm for a feed forward neural network with a single hidden layer of nodes (i.e., two layers of connection weights). Our algorithm is capable of training networks for hard problems, such as the classic two-spirals problem.

The weights in the first layer are determined using a quasirandom number generator. These weights are frozen—they are never modified during the training process.

The second layer of weights is trained as a simple linear discriminator using methods such as the pseudoinverse, with possible iterations.

We also study the problem of reducing the hidden layer: pruning lowweight nodes and a genetic algorithm search for good subsets.

This work was done jointly with Sanjay Raghavendra, Ming Ge, Meiling Lung, and Roger Gaborski.

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