DocumentCode
1264430
Title
The Stone-Weierstrass theorem and its application to neural networks
Author
Cotter, Neil E.
Author_Institution
Dept. of Electr. Eng., Utah Univ., Salt Lake City, UT, USA
Volume
1
Issue
4
fYear
1990
fDate
12/1/1990 12:00:00 AM
Firstpage
290
Lastpage
295
Abstract
The Stone-Weierstrass theorem and its terminology are reviewed, and neural network architectures based on this theorem are presented. Specifically, exponential functions, polynomials, partial fractions, and Boolean functions are used to create networks capable of approximating arbitrary bounded measurable functions. A modified logistic network satisfying the theorem is proposed as an alternative to commonly used networks based on logistic squashing functions
Keywords
Boolean functions; neural nets; parallel architectures; polynomials; Boolean functions; Stone-Weierstrass theorem; architectures; exponential functions; logistic network; logistic squashing functions; neural networks; partial fractions; polynomials; Boolean functions; Cities and towns; Computer architecture; Computer networks; Hypercubes; Logistics; Neural networks; Polynomials; Prototypes; Terminology;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.80265
Filename
80265
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