DocumentCode
2139792
Title
Clustering in product space for fuzzy inference
Author
Berenji, Hamid R. ; Khedkar, Pratap S.
Author_Institution
NASA Ames Res. Center, Mountain View, CA, USA
fYear
1993
fDate
1993
Firstpage
1402
Abstract
The authors present an algorithm that generates a set of fuzzy rules with linear consequents from raw data using radial basis functions and an extended clustering approach. The algorithm uses output information in conjunction with adding and pruning neurons to generate a compact structure and its rough approximation quickly from one pass over the data. It is shown that this algorithm can approximate a typical nonlinear switching function by generating a set of fuzzy rules
Keywords
fuzzy logic; inference mechanisms; neural nets; uncertainty handling; clustering; fuzzy inference; fuzzy rules; neural nets; nonlinear switching function; product space; radial basis functions; rough approximation; Approximation algorithms; Artificial intelligence; Clustering algorithms; Data mining; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Inference algorithms; NASA; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 1993., Second IEEE International Conference on
Conference_Location
San Francisco, CA
Print_ISBN
0-7803-0614-7
Type
conf
DOI
10.1109/FUZZY.1993.327598
Filename
327598
Link To Document