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Proceedings Paper

Fuzzy-rough membership function neural network and its application to pattern recognition
Author(s): Dongbo Zhang; Yaonan Wang; Huixian Huang
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Paper Abstract

Generally, while designing pattern classifier, the boundaries between different classes are vague and it is often difficult or impossible to acquire all of the necessary essential features for precisely classifying, so often both the fuzzy uncertainty and rough uncertainty are exist in classification problems. In this work, a novel FRMFN (Fuzzy-Rough Membership Function Neural Network) is built based on fuzzy-rough sets theory. The FRMFN integrates the ability of processing fuzzy and rough information simultaneously. The test results of classification for infrared band combination image of Canada Norman Wells area and five vowel characters indicate that FRMFN has better classification precision than RBFN (Radial Basis Function Neural Network) and has the same merit of quick learning as RBFN.

Paper Details

Date Published: 15 November 2007
PDF: 8 pages
Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67882N (15 November 2007); doi: 10.1117/12.748862
Show Author Affiliations
Dongbo Zhang, Xiangtan Univ. (China)
Yaonan Wang, Hunan Univ. (China)
Huixian Huang, Xiangtan Univ. (China)

Published in SPIE Proceedings Vol. 6788:
MIPPR 2007: Pattern Recognition and Computer Vision
S. J. Maybank; Mingyue Ding; F. Wahl; Yaoting Zhu, Editor(s)

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