DocumentCode :
428408
Title :
Neuro-fuzzy extraction of interpretable fuzzy rules from data
Author :
Riid, Andri ; Rustern, Ennu
Author_Institution :
Dept. of Comput. Control, Tallinn Univ. of Technol., Estonia
Volume :
3
fYear :
2004
fDate :
10-13 Oct. 2004
Firstpage :
2266
Abstract :
The paper addresses extraction of linguistic fuzzy rules from data, paying specific attention to such properties of the resulting fuzzy model as interpretability and generalization ability. A modeling technique, combining some previously known heuristic modeling approaches, is developed. Experiments of controller identification based on the truck backer-upper application demonstrate that the proposed technique is able to capture the relevant information even if the data sets used for model extraction are insufficient and/or contain noise.
Keywords :
fuzzy logic; fuzzy set theory; fuzzy systems; identification; knowledge acquisition; Neuro-fuzzy extraction; controller identification; fuzzy model; interpretable fuzzy rules; linguistic fuzzy rules; model extraction; modeling technique; truck backer-upper application; Approximation algorithms; Approximation error; Context modeling; Data mining; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Humans; Training data;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems, Man and Cybernetics, 2004 IEEE International Conference on
ISSN :
1062-922X
Print_ISBN :
0-7803-8566-7
Type :
conf
DOI :
10.1109/ICSMC.2004.1400666
Filename :
1400666
Link To Document :
بازگشت