Title :
An adaptive rule extraction with the fuzzy self-organizing map and a comparison with other methods
Author :
Nomura, Tatsuya ; Miyoshi, Tsutomiu
Author_Institution :
Software Lab., Sharp Corp., Nara, Japan
Abstract :
For automatic rule extraction from a set of input-output data examples, decision tree generating methods such as ID3 and fuzzy ID3 play a major role. These methods, however, are difficult to apply when there is a tendency for the examples to change dynamically. This paper presents a new method for adaptive rule extraction with the fuzzy self-organizing map and the results of simulations in order to present its effectiveness by a comparison with other methods such as RBF (radial basis functions) and GA (genetic algorithms). We obtained the result that our method is superior to other methods for automatic and adaptive rule extraction
Keywords :
adaptive systems; feedforward neural nets; fuzzy neural nets; genetic algorithms; knowledge acquisition; self-organising feature maps; trees (mathematics); automatic adaptive rule extraction; decision tree generating methods; dynamically changing examples; fuzzy ID3; fuzzy self-organizing map; genetic algorithms; input-output data; radial basis functions; simulations; Artificial intelligence; Classification tree analysis; Data mining; Decision trees; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Laboratories; Neural networks; Space technology;
Conference_Titel :
Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society. Proceedings of ISUMA - NAFIPS '95., Third International Symposium on
Conference_Location :
College Park, MD
Print_ISBN :
0-8186-7126-2
DOI :
10.1109/ISUMA.1995.527713
