DocumentCode
2617082
Title
Implementing the fuzzy quadratic classifier
Author
Kersten, Paul R.
Author_Institution
Weapons Div., Naval Air Warfare Center, China Lake, CA, USA
fYear
1997
fDate
21-24 Sep 1997
Firstpage
205
Lastpage
210
Abstract
The fuzzy quadratic classifier (FQC) extends the quadratic classifier to accommodate a sequence of fuzzy numbers as data. The fuzzy data induces a fuzzy quadratic discriminant (QD) that is then compared to other fuzzy discriminants to produce a decision. To generate the fuzzy data, conversion of batches of data vectors, or signals, into sequences of fuzzy signal vectors is developed base on fuzzy order statistics. In data rich environment, this conversion both condenses and refines the data stream. An efficient implementation of the FQC is presented based upon a diagonal form of the quadratic classifier and robust estimation of the parameters. A two class problem illustrates the procedure
Keywords
decision theory; estimation theory; fuzzy set theory; parameter estimation; pattern classification; data vectors; decision; diagonal form; fuzzy discriminants; fuzzy numbers; fuzzy quadratic classifier; fuzzy quadratic discriminant; quadratic classifier; robust estimation; Additive noise; Gaussian distribution; Gaussian noise; Lakes; Noise robustness; Parameter estimation; Signal generators; Statistics; Target recognition; Weapons;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society, 1997. NAFIPS '97., 1997 Annual Meeting of the North American
Conference_Location
Syracuse, NY
Print_ISBN
0-7803-4078-7
Type
conf
DOI
10.1109/NAFIPS.1997.624038
Filename
624038
Link To Document