Design techniques for the control of errors in backpropagation neural networks

Daniel C. St. Clair; Gerald E. Peterson; Stephen Aylward; William E. Bond

doi:10.1117/12.152636

19 August 1993 Design techniques for the control of errors in backpropagation neural networks

Daniel C. St. Clair, Gerald E. Peterson, Stephen Aylward, William E. Bond

Proceedings Volume 1966, Science of Artificial Neural Networks II; (1993) https://doi.org/10.1117/12.152636
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

A significant problem in the design and construction of an artificial neural network for function approximation is limiting the magnitude and variance of errors when the network is used in the field. Network errors can occur when the training data does not faithfully represent the required function due to noise or low sampling rates, when the network's flexibility does not match the variability of the data, or when the input data to the resultant network is noisy. This paper reports on several experiments whose purpose was to rank the relative significance of these error sources and thereby find neural network design principles for limiting the magnitude and variance of network errors.

Citation Download Citation

Daniel C. St. Clair, Gerald E. Peterson, Stephen Aylward, and William E. Bond "Design techniques for the control of errors in backpropagation neural networks", Proc. SPIE 1966, Science of Artificial Neural Networks II, (19 August 1993); https://doi.org/10.1117/12.152636

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