An algorithmic approach for fuzzy inference
- 1 January 1997
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Fuzzy Systems
- Vol. 5 (4) , 585-598
- https://doi.org/10.1109/91.649911
Abstract
To apply fuzzy logic, two major tasks need to be performed: the derivation of production rules and the determination of membership functions. These tasks are often difficult and time consuming. This paper presents an algorithmic method for generating membership functions and fuzzy production rules; the method includes an entropy minimization for screening analog values. Membership functions are derived by partitioning the variables into the desired number of fuzzy terms and production rules are obtained from minimum entropy clustering decisions. In the rule derivation process, rule weights are also calculated. This algorithmic approach alleviates many problems in the application of fuzzy logic to binary classificationKeywords
This publication has 7 references indexed in Scilit:
- Predicting chaotic time series with fuzzy if-then rulesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Selecting fuzzy rules by genetic algorithm for classification problemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- ANFIS: adaptive-network-based fuzzy inference systemIEEE Transactions on Systems, Man, and Cybernetics, 1993
- Generating fuzzy rules by learning from examplesIEEE Transactions on Systems, Man, and Cybernetics, 1992
- A Comparative Analysis of Selection Schemes Used in Genetic AlgorithmsPublished by Elsevier ,1991
- Fuzzy logic in control systems: fuzzy logic controller. IIEEE Transactions on Systems, Man, and Cybernetics, 1990
- Neural network ensemblesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1990