Modelling the XOR/XNOR Boolean functions complexity using neural network

P. W.C. Prasad, A. K. Singh, Azam Beg, Ali Assi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    8 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationICECS 2006 - 13th IEEE International Conference on Electronics, Circuits and Systems
    Pages1348-1351
    Number of pages4
    DOIs
    Publication statusPublished - Dec 1 2006
    EventICECS 2006 - 13th IEEE International Conference on Electronics, Circuits and Systems - Nice, France
    Duration: Dec 10 2006Dec 13 2006

    Publication series

    NameProceedings of the IEEE International Conference on Electronics, Circuits, and Systems

    Other

    OtherICECS 2006 - 13th IEEE International Conference on Electronics, Circuits and Systems
    Country/TerritoryFrance
    CityNice
    Period12/10/0612/13/06

    ASJC Scopus subject areas

    • Engineering(all)

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