Evaluation of the 'Neural Gas' network vector quantization and approximation components

Author

Michael Thomas Cozart

Date of Award

5-1996

Degree Type

Thesis

Degree Name

Master of Science

Major

Computer Science

Major Professor

Bruce Whitehead

Committee Members

Dinesh Mehta, Al Pujol

Abstract

One neural networking design technique for the prediction of nonlinear time-series implements two primary computational components, vector quantization and function approximation. A "Neural Gas" network which uses this design technique has reportedly produced superior prediction results of time-series. This research evaluates the effectiveness or influence of each component of the Neural Gas network by substituting other reputable methods of vector quantization and approximation for those of the Neural Gas network. The substitute quantization component is the Generalized Learning Vector Quantizer. The substitute approximation component is Radial Basis Functions. Results of this research indicate the approximation component of the Neural Gas network contributes most to the excellent prediction results. The prediction results from the substituted components versus the Neural Gas components are discussed.

Recommended Citation

Cozart, Michael Thomas, "Evaluation of the 'Neural Gas' network vector quantization and approximation components. " Master's Thesis, University of Tennessee, 1996.
https://trace.tennessee.edu/utk_gradthes/10796