  In recent years, many neural network models have been proposed for pattern
classification, function approximation and regression problems. This paper
presents an approach for classifying patterns from simplified NNs. Although the
predictive accuracy of ANNs is often higher than that of other methods or human
experts, it is often said that ANNs are practically "black boxes", due to the
complexity of the networks. In this paper, we have an attempted to open up
these black boxes by reducing the complexity of the network. The factor makes
this possible is the pruning algorithm. By eliminating redundant weights,
redundant input and hidden units are identified and removed from the network.
Using the pruning algorithm, we have been able to prune networks such that only
a few input units, hidden units and connections left yield a simplified
network. Experimental results on several benchmarks problems in neural networks
show the effectiveness of the proposed approach with good generalization
ability.
