Cart (Loading....) | Create Account
Close category search window
 

Simple approximation of sigmoidal functions: realistic design of digital neural networks capable of learning

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)
Alippi, C. ; Dipartimento di Elettronica, Politecnico di Milano, Italy ; Storti-Gajani, G.

Two different approaches to nonlinearity simplification in neural nets are presented. Both the solutions are based on approximation of the sigmoidal mapper often used in neural networks (extensions are being considered to allow approximation of a more general class of functions). In particular, a first solution yielding a very simple architecture, but involving discontinuous functions is presented; a second solution, slightly more complex, but based on a continuous function is then presented. This second solution has been successfully used in conjunction with the classical generalized delta rule algorithm

Published in:

Circuits and Systems, 1991., IEEE International Sympoisum on

Date of Conference:

11-14 Jun 1991

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.