Generalisation of A Class of ContinuousNeural

We propose a way of using boolean circuits to perform real valued computation in a way that naturally extends their boolean func-tionality. The functionality of multiple fan in threshold gates in this model is shown to mimic that of a hardware implementation of continuous Neural Networks. A Vapnik-Chervonenkis dimension and sample size analysis for the system is performed giving best known sample sizes for a real valued Neural Network. Experimental results connrm the conclusion that the sample sizes required for the networks are signiicantly smaller that for sigmoidal networks.