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.