Title

The General Approximation Theorem

Abstract

A general approximation theorem is proved. It uniformly envelopes both the classical Stone theorem and approximation of functions of several variables by means of superpositions and linear combinations of functions of one variable. This theorem is interpreted as a statement on universal approximating possibilities ("approximating omnipotence") of arbitrary nonlinearity. For the neural networks, our result states that the function of neuron activation must be nonlinear, and nothing else.

Meeting Name

IEEE World Congress on Computational Intelligence (WCCI'98) (1998: May 4-9, Anchorage, AK)

Department(s)

Electrical and Computer Engineering

International Standard Book Number (ISBN)

0000780348591

International Standard Serial Number (ISSN)

1098-7576

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 1998 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.


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