Dynamically pruning output weights in an expanding multilayer perceptron neural network

The network size for a multilayer perceptron neural network is often chosen arbitrarily for different applications, and the optimum size of the network is determined by a long process of trial and error. This paper presents a backpropagation algorithm. For a multilayer perceptron (MLP) neural network, that dynamically determines the optimum number of hidden nodes and applies a new pruning technique on output weights. A 29% reduction in the total number of output weights was observed for a handwritten character recognition problem using the new pruning algorithm