By Topic

Approximation to Boolean functions by neural networks with applications to thinning algorithms

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Xiong Shenshu ; Dept. of Precision Instrum. & Mechanology, Tsinghua Univ., Beijing, China ; Zhou Zhaoying ; Zhong Limin ; Zhang Wendong

In this paper, a theorem on approximation to Boolean functions by neural networks and its proof are proposed. A Boolean function, f:{0,1}n→{0,1} is proved to be approximated by a three layer neural network with 2n hidden nodes. With the theorem, a thinning algorithm using the neural network technique is concluded. A hard processor implementing the thinning algorithm is designed to raise the thinning efficiency, which can meet the practical needs better. This makes the algorithm suitable for real-time image processing

Published in:

Instrumentation and Measurement Technology Conference, 2000. IMTC 2000. Proceedings of the 17th IEEE  (Volume:2 )

Date of Conference: