Fuzzy modeling by induction and pruning of decision trees

Presents a Fuzzy ID3 algorithm that generates a fuzzy rule base from a set of input-output data. The rules are induced by the ID3 algorithm of Quinlan (1986). Pruning conditions are presented that assist in the elimination of irrelevant attributes and simplification of the rule base. The algorithm is applied to the modeling of an ARMA process with both known and unknown model order.<<ETX>>

[1]  W. Pedrycz.  An identification algorithm in fuzzy relational systems , 1984 .

[2]  Witold Pedrycz.  Identification in fuzzy systems , 1984, IEEE Transactions on Systems, Man, and Cybernetics.

[3]  Yong-Zai Lu,et al.  Fuzzy Model Identification and Self-Learning for Dynamic Systems , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[4]  Douglas H. Fisher,et al.  Concept Simplification and Prediction Accuracy , 1988, ML.

[5]  Caroline Chan,et al.  Determination of quantization intervals in rule based model for dynamic systems , 1991, Conference Proceedings 1991 IEEE International Conference on Systems, Man, and Cybernetics.

[6]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1991, Proceedings of the 1991 IEEE International Symposium on Intelligent Control.

[7]  Celal Batur,et al.  Automated rule-based model generation for uncertain complex dynamic systems , 1991 .

[8]  Kazuo Tanaka,et al.  Stability analysis and design of fuzzy control systems , 1992 .

[9]  Kazuo Tanaka,et al.  Fuzzy modeling by ID3 algorithm and its application to prediction of heater outlet temperature , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[10]  Michio Sugeno,et al.  A fuzzy-logic-based approach to qualitative modeling , 1993, IEEE Trans. Fuzzy Syst..

[11]  Shyh Hwang,et al.  An identification algorithm in fuzzy relational systems , 1996, Soft Computing in Intelligent Systems and Information Processing. Proceedings of the 1996 Asian Fuzzy Systems Symposium.