Design rules of multilayer perceptrons

Youngjik I. Lee; San-Hoon Oh; Hyun Kyung Song; Myung Won Kim

doi:10.1117/12.140099

1 July 1992 Design rules of multilayer perceptrons

Youngjik I. Lee, San-Hoon Oh, Hyun Kyung Song, Myung Won Kim

Proceedings Volume 1710, Science of Artificial Neural Networks; (1992) https://doi.org/10.1117/12.140099
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

Multilayer perceptrons with the error back-propagation learning algorithm are widely used for many pattern classification applications. In this paper, we address some design rules of a multilayer perceptron related to its learning speed and selection of an optimal number of hidden nodes. One of the critical drawbacks of the error back-propagation learning algorithm is its slow learning speed. We have analyzed the reasons for this drawback, and suggested that fast learning can be achieved with proper initial weight settings. Another important problem for multilayer networks is to determine an optimal number of hidden nodes. By analyzing the total error of a multilayer perceptron, we propose an efficient method which yields to an appropriate number of hidden nodes by iteratively eliminating unnecessary hidden nodes. These design rules have been successfully applied to the handwritten digit recognition problem.

Citation Download Citation

Youngjik I. Lee, San-Hoon Oh, Hyun Kyung Song, and Myung Won Kim "Design rules of multilayer perceptrons", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140099

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available