By Topic

An optimal method for linear threshold neural network synthesis

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
$31 $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

2 Author(s)
Rhee, F.C.-H. ; Dept. of Electron. Eng., Hanyang Univ., Ansan, South Korea ; Byeong-Jun Park

In the digital design area the minimized function of binary variables may be represented by two levels of AND/OR gates. However, depending upon the application, the design may require a large number of gates. We propose a method that is capable of reducing the required number of gates necessary to realize an N-dim binary function by implementing linear threshold units. Hence, we propose an approach for obtaining a minimal linear threshold neural network from a binary pattern space. The method is based on optimal groupings of minimal-sum-of-product (MSP) terms of a function represented by binary class patterns. In doing so, we are able to obtain a fast realization of a linear threshold neural network. Several experimental results are given

Published in:

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on  (Volume:3 )

Date of Conference:

4-9 May 1998