Title :
Generating fuzzy rules for a neural fuzzy classifier
Author :
Li, Chihwen Chris ; Wu, Chwan-Hwa John
Author_Institution :
Dept. of Electr. Eng., Nat. I-Lan Inst. of Agric. & Technol., Taiwan, China
Abstract :
It is difficult to design a classifier when overlap problems occur between the decision regions of different classes. A top-down learning procedure is described which trains a neural fuzzy (NF) system from global to local views of the overlap decision region, and generates nested IF-THEN rules. With these rules, the NF system can correctly separate similar classes within the overlap decision region. Two operational examples of the NF system are given
Keywords :
content-addressable storage; fuzzy logic; fuzzy neural nets; hierarchical systems; learning (artificial intelligence); pattern classification; fuzzy associative memory; fuzzy rules generation; global views; local views; nested IF-THEN rules; neural fuzzy classifier; operational examples; overlap decision region; top-down learning procedure; training; Adaptive systems; Agriculture; Associative memory; Data engineering; Fuzzy control; Fuzzy reasoning; Fuzzy sets; Fuzzy systems; Noise measurement; Programmable control;
Conference_Titel :
Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1896-X
DOI :
10.1109/FUZZY.1994.343597
