Analysis of feedforward networks

Ronald K. Pearson

doi:10.1117/12.130827

16 December 1992 Analysis of feedforward networks

Ronald K. Pearson

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130827
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

Feedforward networks are used extensively in practice to learn static mappings between related sets of variables. These networks are difficult to analyze, however, both because of their nonlinearity and their complex interconnection structure. In the absence of the nonlinearity, linear algebra could provide considerable insight into the behavior of these networks, significantly beyond that possible from a detailed analysis of individual neurons. Such insights would be extremely valuable since the power of neural networks arises from their large-scale connectivity, rather than the inherent computational capacity of the individual neurons. This paper proposes algebraic category theory as the basis for obtaining such global insights for feedforward networks in spite of their nonlinearity.

Citation Download Citation

Ronald K. Pearson "Analysis of feedforward networks", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130827

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