Masters Theses

Keywords and Phrases

Adaptive Resonance Theory; Clustering; Particle Swarm Optimization; Time Series Prediction

Abstract

"Adaptive Resonance Theory, ART, is a powerful clustering tool for learning arbitrary patterns in a self-organizing manner. In this research, two papers are presented that examine the extensibility and applications of ART. The first paper examines a means to boost ART performance by assigning each cluster a vigilance value, instead of a single value for the whole ART module. A Particle Swarm Optimization technique is used to search for desirable vigilance values. In the second paper, it is shown how ART, and clustering in general, can be a useful tool in preprocessing time series data. Clustering quantization attempts to meaningfully group data for preprocessing purposes, and improves results over the absence of quantization with statistical significance."--Abstract, page iv.

Advisor(s)

Wunsch, Donald C.

Committee Member(s)

Stanley, R. Joe
Olbricht, Gayla R.

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Computer Engineering

Sponsor(s)

M.K. Finley Missouri Endowment

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2015

Journal article titles appearing in thesis/dissertation

  • Particle swarm optimization in an adaptive resonance framework
  • Time series prediction via two-step clustering

Pagination

ix, 33 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2015 Clayton Parker Smith, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Library of Congress Subject Headings

Pattern recognition systems
Machine learning
Swarm intelligence
Time-series analysis

Thesis Number

T 10696

Electronic OCLC #

913515664

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