Rough Neuron Based Neural Classifier

Abstract

Rough sets theory can be applied to the problem of pattern recognition using neural networks in three different stages: preprocessing, learning rule and in the architecture. This paper discusses the use of rough set theory in the architecture of the unsupervised neural network, which is implemented, by the use of rough neuron. The rough neuron consists of two neurons: upper boundary neuron and lower boundary neuron, derived on the upper and lower boundaries of the input vector. The proposed neural network uses the Kohonen learning rule. Problem of character recognition is taken to verify the usefulness of such a network. The data set is formed by the images of English alphabets of ten different fonts. The approximation quality of such a network is better compared to the traditional networks. The number of iterations reduce significantly for such a network and hence the convergence time.