Modelling the XOR/XNOR Boolean Functions Complexity Using Neural Network


Prasad, P. W. C. and Singh, A. K. and Beg, Azam and Assi, Ali (2006) Modelling the XOR/XNOR Boolean Functions Complexity Using Neural Network. In: 3th IEEE International Conference on Electronics, Circuits and Systems.

Full text not available from this repository.


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.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Information Science and Technology (FIST)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 21 Sep 2011 08:22
Last Modified: 21 Sep 2011 08:22


Downloads per month over past year

View ItemEdit (login required)