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http://dx.doi.org/10.6109/jkiice.2007.11.5.969

Image Recognition by Fuzzy Logic and Genetic Algorithms  

Ryoo, Sang-Jin (전남대학교)
Na, Chul-Hoon (목포대학교)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.11, no.5, 2007 , pp. 969-976 More about this Journal
Abstract
A fuzzy classifier which needs various analyses of features using genetic algorithms is proposed. The fuzzy classifier has a simple structure, which contains a classification part based on fuzzy logic theory and a rule generation part using genetic algorithms. The rule generation part determines optimal fuzzy membership functions and inclusion or exclusion of each feature in fuzzy classification rules. We analyzed recognition rate of a specific object, then added finer features repetitively, if necessary, to the object which has large misclassification rate. And we introduce repetitive analyses method for the minimum size of string and population, and for the improvement of recognition rates. This classifier is applied to two examples of the recognition of iris data and the recognition of Thyroid Gland cancer cells. The fuzzy classifier proposed in this paper has recognition rates of 98.67% for iris data and 98.25% for Thyroid Gland cancer cells.
Keywords
Recognition; Fuzzy Logic; Genetic Algorithm;
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