1
Department of Electrical Engineering, University of Birjand, Birjand, Iran.
2
Department of Electrical and Computer Engineering, Faculty of Engineering, University of Birjand, Shoukat-Abad, Birjand, Iran.
Abstract
Fuzzy classifiers as a kind of fuzzy systems are powerful approaches in pattern recognition tasks. These classifiers consist of various structural parameters, each of them have major effects on the performance of fuzzy classifiers. Type and locations of membership functions, in addition to fuzzy antecedents and consequents are most important of these structural parameters. Usually, the major problem in design and implementation of fuzzy classifiers is optimum setting up of these parameters, to reach the best performance. In this paper, a method is described for estimation of optimum aforementioned fuzzy parameters in a fuzzy classifier. Extensive experimental results are presented to show the effectiveness and powerfulness of the proposed method.
askari, H. & Zahiri, S. (2012). Using Swarm Intelligence Approach in the Optimal Design of Fuzzy Rule-Based Classifier Systems. Journal of Soft Computing and Information Technology, 1(1), 89–100.
MLA
askari, H., & Zahiri, S. "Using Swarm Intelligence Approach in the Optimal Design of Fuzzy Rule-Based Classifier Systems", Journal of Soft Computing and Information Technology, 1, 1, 2012, 89–100.
HARVARD
askari, H., & Zahiri, S. (2012). 'Using Swarm Intelligence Approach in the Optimal Design of Fuzzy Rule-Based Classifier Systems', Journal of Soft Computing and Information Technology, 1(1), pp. 89–100.
CHICAGO
askari, H. & Zahiri, S., "Using Swarm Intelligence Approach in the Optimal Design of Fuzzy Rule-Based Classifier Systems." Journal of Soft Computing and Information Technology, 1 1 (2012): 89–100,
VANCOUVER
askari H., Zahiri S. Using Swarm Intelligence Approach in the Optimal Design of Fuzzy Rule-Based Classifier Systems. JSCIT. 2012;1(1):89–100 (In Persian).
