Supervised Competitive Learning with Backpropagation Network and Fuzzy Logic

Authors

Takayuki Dan Kimura, Washington University in St Louis
Thomas H. Fuller Jr., Washington University in St Louis
Ce Wang, Washington University in St. Louis

Document Type

Technical Report

Department

Computer Science and Engineering

Publication Date

1993-01-01

Filename

WUCS-93-17.PDF

Technical Report Number

WUCS-93-17

Abstract

SCL assembles a set of learning modules into a supervised learning system to address the stability-plasticity dilemma. Each learning module acts as a similarity detector for a prototype, and includes prototype resetting (akin to that of ART) to respond to new prototypes. Here (Part I) we report SCL results using back-propagation networks as the learning modules. We used two feature extractors: about 30 energy-based features, and a combination of energy-based and graphical features (about 60). ACL recognized 96% (energy) and 99% (energy/graphical) of test digits, and 91% (energy) and 96% (energy/graphical) of test letters. In the accompanying paper (Part II), we report the results of SCL using fuzzy sets as learning moduels for recognizing handwritten digits.

Comments

Permanent URL: http://dx.doi.org/10.7936/K7T151W0

Recommended Citation

Kimura, Takayuki Dan; Fuller, Thomas H. Jr.; and Wang, Ce, "Supervised Competitive Learning with Backpropagation Network and Fuzzy Logic" Report Number: WUCS-93-17 (1993). All Computer Science and Engineering Research.
https://openscholarship.wustl.edu/cse_research/304