Dynamic Growth of Hidden-Layer Neurons Using the Non-extensive Entropy

In this paper we present a dynamic neural network that dynamically grows the number of the hidden-layer neurons based on an increase in the entropy of the weights during training. The weights are normalized to probability values prior to the computation of the entropy. The entropy being referred is the non-extensive entropy proposed recently by Susan and Hanmandlu for the representation of structured data. Incrementally growing the hidden layer as per requirement leads to better tuning of network weights and high classification performance as proved by the empirical results.