Rule Base Completion in Fuzzy Models
- 177 Downloads
Approximation theory based on fuzzy sets provides a mathematical tool for modeling complex systems. A fuzzy model is defined by a family of rules whose antecedents consist of fuzzy sets that partition the input domain of the system. An incomplete model is obtained when the information used to construct the model is insufficient to produce rules for each possible input condition. Rule base completion generates new rules by utilizing the similarity of the antecedents of the existing rules to the conditions for which the rule base specifies no action or response. The effectiveness of completion as a tool for building fuzzy models is demonstrated by two applications. The first incorporates completion into an algorithm that learns fuzzy rules from training data and the second uses completion to modify rules in an adaptive fuzzy system.
Unable to display preview. Download preview PDF.
- M. Mukaidono, L. Ding, and Z. Shen, “Approximate reasoning based on the revision principie,” in Proceedings of NAFIPS’90, (Toronto), pp. 94–97, 1990.Google Scholar
- L. Wang and J. M. Mendel, “Generating fuzzy rules form numerical data, with applications,” Tech. Rep. USC-SIPI-169, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089, 1991.Google Scholar
- R. J. Hammell II, A Fuzzy Associative Memory for Fuzzy Inference and Adaptivity. PhD thesis, Wright State University, Dayton, OH, 1995.Google Scholar
- R. J. Hammell II and T. Sudkamp, “A two-level architecture for fuzzy learning,” Journal of Intelligent and Fuzzy Systems, to appear, 1995.Google Scholar