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
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;
Conference_Titel :
Fuzzy Systems, 1993., Second IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0614-7
DOI :
10.1109/FUZZY.1993.327598
