A neural architecture for fuzzy classification with application to complex system tracking

Author

Stadter, Patrick A. ; Garga, Amulya K.

Author_Institution

Appl. Res. Lab., Pennsylvania State Univ., University Park, PA, USA

Volume

4

fYear

1997

fDate

9-12 Jun 1997

Firstpage

2439

Abstract

The application of a new architecture for fuzzy pattern classification is described to address the problem of tracking complex, discrete event driven systems. The classifier relies upon the integration of fuzzy logic techniques with an artificial neural architecture to produce an efficient mechanism for classifying input patterns. In addition, the fuzzy classifier provides a method for quantifying and handling ambiguity near the decision surfaces. The proposed application consists of classifying input feature patterns as events which drive complex, dynamic systems modeled as discrete event systems

Keywords

computational geometry; discrete event systems; fuzzy logic; fuzzy neural nets; large-scale systems; neural net architecture; pattern classification; ambiguity; complex system tracking; discrete event driven systems; fuzzy logic techniques; fuzzy pattern classification; neural architecture; Artificial neural networks; Automatic control; Computer architecture; Discrete event systems; Drives; Fuzzy logic; Fuzzy systems; Pattern classification; Pattern recognition; Vehicle dynamics;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.614464

Filename

614464