Keywords and Phrases
Adaptive Resonance Theory; Clustering; Particle Swarm Optimization; Time Series Prediction
"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.
Wunsch, Donald C.
Stanley, R. Joe
Olbricht, Gayla R.
Electrical and Computer Engineering
M.S. in Computer Engineering
M.K. Finley Missouri Endowment
Missouri University of Science and Technology
Journal article titles appearing in thesis/dissertation
- Particle swarm optimization in an adaptive resonance framework
- Time series prediction via two-step clustering
ix, 33 pages
© 2015 Clayton Parker Smith, All rights reserved.
Thesis - Open Access
Library of Congress Subject Headings
Pattern recognition systems
Electronic OCLC #
Link to Catalog Record
Smith, Clayton Parker, "Fuzzy adaptive resonance theory: Applications and extensions" (2015). Masters Theses. 7418.