Neural networks for classification
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Authors
Pritchett, William Christopher.
Subjects
Advisors
Date of Issue
1998
Date
Publisher
Monterey California. Naval Postgraduate School
Language
en_US
Abstract
In many applications, ranging from character recognition to signal detection to automatic target identification, the problem of signal classification is of interest. Often, for example, a signal is known to belong to one of a family of sets C sub 1..., C sub n and the goal is to classify the signal according to the set to which it belongs. The main purpose of this thesis is to show that under certain conditions placed on the sets, the theory of uniform approximation can be applied to solve this problem. Specifically, if we assume that sets C sub j are compact subsets of a normed linear space, several approaches using the Stone-Weierstrass theorem give us a specific structure for classification. This structure is a single hidden layer feedforward neural network. We then discuss the functions which comprise the elements of this neural network and give an example of an application
Type
Thesis
Description
CIVINS (Civilian Institutions) Thesis document
Series/Report No
Department
Engineering
Organization
Identifiers
NPS Report Number
Sponsors
Funder
CIVINS
Format
vi, 62 leaves;28 cm.
Citation
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.