A fuzzy-GRNN classifier for pattern recognition

Author

Verma, Prabha ; Singh, Prashant ; Yadava, R.D.S.

Author_Institution

Dept. of Phys., Banaras Hindu Univ., Varanasi, India

fYear

2015

fDate

19-20 Feb. 2015

Firstpage

659

Lastpage

661

Abstract

We present a scheme for combining fuzzy c-means clustering with generalized regression neural network for improving its pattern classification efficiency. The membership grades of data points produced by fuzzy c-means clustering have been used to define weights for data points in the feature space according a logarithmic measure of uncertainty similar to the Shannon´s entropy. The method improves classification results from 1% to 17% for 9 data sets analyzed here.

Keywords

fuzzy set theory; learning (artificial intelligence); neural nets; pattern classification; pattern clustering; regression analysis; Shannon entropy; data point weights; feature space; fuzzy c-means clustering; fuzzy-GRNN classifier; generalized regression neural network; logarithmic uncertainty measure; membership grades; pattern classification efficiency improvement; pattern recognition; Neural networks; Neurons; Pattern recognition; Principal component analysis; Sensors; Signal processing algorithms; Uncertainty; Ferature vector weighting; Fuzzy uncertainty; Generalized regression neural network calssifier;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing and Integrated Networks (SPIN), 2015 2nd International Conference on

Conference_Location

Noida

Print_ISBN

978-1-4799-5990-7

Type

conf

DOI

10.1109/SPIN.2015.7095166

Filename

7095166