Back to Search Start Over

Characterization of Linearly Separable Boolean Functions: A Graph-Theoretic Perspective

Authors :
Xian-Da Zhang
Yanyi Rao
Source :
IEEE Transactions on Neural Networks and Learning Systems. 28:1542-1549
Publication Year :
2017
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2017.

Abstract

In this paper, we present a novel approach for studying Boolean function in a graph-theoretic perspective. In particular, we first transform a Boolean function $f$ of $n$ variables into an induced subgraph $H_{f}$ of the $n$ -dimensional hypercube, and then, we show the properties of linearly separable Boolean functions on the basis of the analysis of the structure of $H_{f}$ . We define a new class of graphs, called hyperstar, and prove that the induced subgraph $H_{f}$ of any linearly separable Boolean function $f$ is a hyperstar. The proposal of hyperstar helps us uncover a number of fundamental properties of linearly separable Boolean functions in this paper.

Details

ISSN :
21622388 and 2162237X
Volume :
28
Database :
OpenAIRE
Journal :
IEEE Transactions on Neural Networks and Learning Systems
Accession number :
edsair.doi.dedup.....1b898d7ce116cfedecdcb997a87c0e3b
Full Text :
https://doi.org/10.1109/tnnls.2016.2542205