Improvement of Pattern Recognition Capacity of the Fuzzy ART with the Variable Learning 


Vol. 38,  No. 12, pp. 954-961, Dec.  2013


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  Abstract

In this paper, we propose a new learning method using a variable learning to improve pattern recognition in the FCSR(Fast Commit Slow Recode) learning method of the Fuzzy ART. Traditional learning methods have used a fixed learning rate in updating weight vector(representative pattern). In the traditional method, the weight vector will be updated with a fixed learning rate regardless of the degree of similarity of the input pattern and the representative pattern in the category. In this case, the updated weight vector is greatly influenced from the input pattern where it is on the boundary of the category. Thus, in noisy environments, this method has a problem in increasing unnecessary categories and reducing pattern recognition capacity. In the proposed method, the lower similarity between the representative pattern and input pattern is, the lower input pattern contributes for updating weight vector. As a result, this results in suppressing the unnecessary category proliferation and improving pattern recognition capacity of the Fuzzy ART in noisy environments.

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  Cite this article

[IEEE Style]

C. j. Lee, B. Son, H. s. Hong, "Improvement of Pattern Recognition Capacity of the Fuzzy ART with the Variable Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 38, no. 12, pp. 954-961, 2013. DOI: .

[ACM Style]

Chang joo Lee, Byounghee Son, and Hee sik Hong. 2013. Improvement of Pattern Recognition Capacity of the Fuzzy ART with the Variable Learning. The Journal of Korean Institute of Communications and Information Sciences, 38, 12, (2013), 954-961. DOI: .

[KICS Style]

Chang joo Lee, Byounghee Son, Hee sik Hong, "Improvement of Pattern Recognition Capacity of the Fuzzy ART with the Variable Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 38, no. 12, pp. 954-961, 12. 2013.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880