Abstract

Artificial neural network algorithms were originally designed to model human neural activities. They attempt to recreate the processes involved in such activities as learning, short term memory, and long term memory. Two widely used artificial neural network algorithms are the Self-Organizing Map (SOM) and the Adaptive Resonance Theory (ART2). Each was designed to simulate a particular biological neural activity. Both can be used as unsupervised data classifiers.

This paper compares performance characteristics of two unsupervised artificial neural network architectures; the SOM and the ART2 networks. The primary factors analyzed were classification accuracy, sensitivity to data noise, and sensitivity to the algorithm control parameters. Guidelines are developed for algorithm selection.

Department(s)

Computer Science

Second Department

Mathematics and Statistics

Comments

The first Author is a Graduate Student.

This report is substantially the M.S. thesis of the first author, completed May 1994.

This thesis has been prepared in the style utilized by the Association for Computing Machinery (ACr-.1). Pages 1-20 were published in the Proceedings of the 1994 ACM Symposium on Applied Computing, 6-8 March 1994, The Appendix has been added for purposes normal to thesis writing.

Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission.

© 1994 ACM 089791-647-6/ 94/ 0003

Report Number

CSc-94-09

Document Type

Technical Report

Document Version

Final Version

File Type

text

Language(s)

English

Rights

© 1994 University of Missouri - Rolla, All rights reserved

Publication Date

1 May, 1994

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