An ordering algorithm for pattern presentation in fuzzy ARTMAP that tends to improve generalization performance

Author

Dagher, Issam ; Georgiopoulos, Michael ; Heileman, Gregory L. ; Bebis, George

Author_Institution

Dept. of Electr. & Comput. Eng., Central Florida Univ., Orlando, FL, USA

Volume

10

Issue

4

fYear

1999

fDate

7/1/1999 12:00:00 AM

Firstpage

768

Lastpage

778

Abstract

We introduce a procedure, based on the max-min clustering method, that identifies a fixed order of training pattern presentation for fuzzy adaptive resonance theory mapping (ARTMAP). This procedure is referred to as the ordering algorithm, and the combination of this procedure with fuzzy ARTMAP is referred to as ordered fuzzy ARTMAP. Experimental results demonstrate that ordered fuzzy ARTMAP exhibits a generalization performance that is better than the average generalization performance of fuzzy ARTMAP, and in certain cases as good as, or better than the best fuzzy ARTMAP generalization performance. We also calculate the number of operations required by the ordering algorithm and compare it to the number of operations required by the training phase of fuzzy ARTMAP. We show that, under mild assumptions, the number of operations required by the ordering algorithm is a fraction of the number of operations required by fuzzy ARTMAP

Keywords

ART neural nets; fuzzy neural nets; generalisation (artificial intelligence); learning (artificial intelligence); pattern clustering; fuzzy ARTMAP; generalization performance; max-min clustering method; ordering algorithm; pattern presentation; training pattern presentation; Clustering algorithms; Clustering methods; Computer science; Control systems; Fuzzy logic; Radar applications; Radar tracking; Resonance; Sonar applications; Subspace constraints;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.774217

Filename

774217