Modelling the XOR/XNOR Boolean Functions Complexity Using Neural Network

This paper propose a model for the complexity of Boolean functions with only XOR/XNOR min-terms using back propagation neural networks (BPNNs) applied to binary decision diagrams (BDDs). The developed BPNN model (BPNNM) is obtained through the training process of experimental data using Brain Maker software package. The outcome of this model is a unique matrix for the complexity estimation over a set of BDDs derived from randomly generated Boolean expressions with a given number of variables and XOR/XNOR min-terms. The comparison results of the experimental and back propagation neural networks mode (BPNNM) underline the efficiency of this approach, which is capable of providing some useful clues about the complexity of the final circuit implementation.