Fuzzy-rough membership function neural network and its application to pattern recognition

Dongbo Zhang; Yaonan Wang; Huixian Huang

doi:10.1117/12.748862

15 November 2007 Fuzzy-rough membership function neural network and its application to pattern recognition

Dongbo Zhang, Yaonan Wang, Huixian Huang

Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 67882N (2007) https://doi.org/10.1117/12.748862
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

Citation Download Citation

Dongbo Zhang, Yaonan Wang, and Huixian Huang "Fuzzy-rough membership function neural network and its application to pattern recognition", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 67882N (15 November 2007); https://doi.org/10.1117/12.748862

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