Title :
A hybrid design method of fuzzy systems
Author :
Li, Ying ; Zhao, Rongchun ; Zhang, Yanning
Author_Institution :
Sch. of Comput., Northwestern Polytech. Univ., Xi´´an, China
fDate :
31 Aug.-4 Sept. 2004
Abstract :
A hybrid approach for designing fuzzy rule-based systems based on clustering and a class of fuzzy neural networks is introduced. Firstly, an unsupervised clustering technique is used to determine the number of fuzzy rules and generate an initial fuzzy rule base from the given input-output data. Secondly, a class of fuzzy neural networks is constructed and its weights are tuned to make the parameters of the constructed fuzzy rule base more precise. Finally, we focus on function approximation problems as a vehicle to evaluate its performance.
Keywords :
fuzzy neural nets; fuzzy systems; knowledge based systems; pattern clustering; unsupervised learning; function approximation; fuzzy neural networks; fuzzy rule-based system; fuzzy system design; unsupervised clustering technique; Clustering algorithms; Design methodology; Equations; Fuzzy neural networks; Fuzzy systems; Input variables; Knowledge based systems; Performance analysis; Vehicles;
Conference_Titel :
Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on
Print_ISBN :
0-7803-8406-7
DOI :
10.1109/ICOSP.2004.1441641
