Title :
Fast neuro-fuzzy classifier
Author :
Gorshkov, Ye V. ; Kokshenev, I.V. ; Rudnyeva, O.O.
Author_Institution :
Artificial Intelligence Dept., Kharkiv Nat. Univ. of Radioelectronics, Ukraine
Abstract :
The problem of data classification with the help of neuro-fuzzy and clustering techniques is considered. The architecture of a neuro-fuzzy classifier is proposed. It is characterized by the incorporation of possibilistic information into the consequents of classification rules. Such information can be helpful in the interpretation of classification results. Comparison with some known neuro-fuzzy classification schemes is given. Special emphasis is placed on the learning speed, which can be critical when the learning dataset is large. Experimental results confirm the correctness of the theoretical assumptions.
Keywords :
fuzzy neural nets; learning (artificial intelligence); pattern classification; pattern clustering; classification rules; clustering techniques; data classification; fast neuro-fuzzy classifier; learning dataset; learning speed; neuro-fuzzy techniques; possibilistic information; Artificial intelligence; Control systems; Distortion measurement; Fuzzy control; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Medical diagnosis; Multidimensional systems; Noise measurement;
Conference_Titel :
Neural Engineering, 2003. Conference Proceedings. First International IEEE EMBS Conference on
Print_ISBN :
0-7803-7579-3
DOI :
10.1109/CNE.2003.1196885
