Using Individually Tested Rules for the Data-based Generation of Interpretable Rule Bases with High Accuracy

Author:

Slawinski Timo,Krause Peter,Kiendl Harro

Publisher

Springer Berlin Heidelberg

Reference18 articles.

1. A. Krone and H. Kiendl. Automatic generation of positive and negative rules for two—way fuzzy controllers. In Proceedings of the Second European Congress on Intelligent Techniques and Soft Computing (EUFIT ’94), volume 1, pages 438–447, Aachen, 1994. Verlag Mainz.

2. T. Slawinski, A. Krone, P. Krause, and H. Kiendl. The fuzzy—rosa method: A statistically motivated fuzzy approach for data—based generation of small interpretable rule bases in high—dimensional search spaces. In M. Last, A. Kandel, and H. Bunke, editors, Data Mining and Computational Intelligence, pages 141–166. Physica-Verlag, Heidelberg, 2001.

3. A. Krone and H. Taeger. Data-based fuzzy rule test for fuzzy modelling. Fuzzy Sets and Systems, 126:343–358, 2001.

4. H. Jessen. Test and rating strategies for automatic fuzzy rule generation and application to load prediction. In M. Mohammadian, editor, New Frontiers in Computational Intelligence and its Applications, pages 11–21. IOS Press, Amsterdam, Niederlande, 2000.

5. H. Kiendl. Decision analysis by advanced fuzzy systems. In J. Kacprzyk L. Zadeh, editor, Computing with Words in Information/Intelligent Systems, pages 223–242. Physica-Verlag, Heidelberg, 1999.

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