| Title |
Design of Radial Basis Function with the Aid of Fuzzy KNN and Conditional FCM |
| Authors |
노석범(Roh, Seok-Beon) ; 오성권(Oh, Sung-Kwun) |
| Keywords |
Radial Basis Function Neural Networks(RBFNN) ; Conditional Fuzzy C-Means(CFCM) ; Fuzzy k-Nearest Neighbors(FKNN) ; Auxiliary Information |
| Abstract |
The performance of Radial Basis Function Neural Networks depends on setting up the Radial Basis Functions over the input space which are the important design procedure of Radial Basis Function Neural Networks. The existing method to initialize the location of the radial basis functions over the input space is to use the conditional fuzzy C-means clustering. However, the researchers which are interested in the conditional fuzzy C-means clustering cannot get as good modeling performance as they expect because the conditional fuzzy C-means clustering cannot project the information which is extracted over the output space into the input space. To compensate the above mentioned drawback of the conditional fuzzy C-means clustering, we apply a fuzzy K-nearest neighbors approach to project the auxiliary information defined over the output space into the input space without lose of the information. |