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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.