Research on Classification Effectiveness of the Novel Mamdani Fuzzy Classifier

Hong Yan Zuo; Zhou Quan Luo; Chao Wu

doi:10.4028/www.scientific.net/AMM.511-512.871

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 511-512Research on Classification Effectiveness of the...

Research on Classification Effectiveness of the Novel Mamdani Fuzzy Classifier

Abstract:

A novel Mamdani fuzzy classifier based on improved chaos immune algorithm is developed, in which bilateral Gaussian membership function parameters are set as constraint conditions and the indexes of fuzzy classification effectiveness and number of correct samples of fuzzy classification as the subgoal of fitness function. Moreover, Iris database is used for classification effectiveness simulation experiment. The results show that Mamdani fuzzy classifier based on improved chaos immune algorithm can effectively improve the prediction accuracy of classification of data sets with noises and outliers.

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Periodical:

Applied Mechanics and Materials (Volumes 511-512)

Pages:

871-874

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.511-512.871

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Online since:

February 2014

Authors:

Hong Yan Zuo*, Zhou Quan Luo, Chao Wu

Keywords:

Fuzzy Classification Effectiveness, Fuzzy Classifier, Fuzzy System, Improved Chaos Immunization Algorithm

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* - Corresponding Author

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