AUTHORS: Ali Assi, P.W.C. Prasad, and Azam Beg
PUBLICATION/VENUE: 7th WSEAS International Conference on Neural Networks (NN'06), Jun 2006, pp. 85-90.
A new neural network (NN) approach is proposed in this paper to estimate the Boolean function (BF) complexity. Large number of randomly generated single output BFs has been used and experimental results show good correlation between the theoretical results and those predicted by the NN model. The proposed model is capable of predicting the number of product terms (NPT) in the BF that gives an indication on its complexity.
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