Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Fuzzy Learning Rule Using the Distance between Datum and the Centroids of Clusters

데이터와 클러스터들의 대표값들 사이의 거리를 이용한 퍼지학습법칙

  • Published : 2007.08.25
Download PDF
⟨ Previous Next ⟩

Abstract

Learning rule affects importantly the performance of neural network. This paper proposes a new fuzzy learning rule that uses the learning rate considering the distance between the input vector and the prototypes of classes. When the learning rule updates the prototypes of classes, this consideration reduces the effect of outlier on the prototypes of classes. This comes from making the effect of the input vector, which locates near the decision boundary, larger than an outlier. Therefore, it can prevents an outlier from deteriorating the decision boundary. This new fuzzy learning rule is integrated into IAFC(Integrated Adaptive Fuzzy Clustering) fuzzy neural network. Iris data set is used to compare the performance of the proposed fuzzy neural network with those of other supervised neural networks. The results show that the proposed fuzzy neural network is better than other supervised neural networks.

학습법칙은 신경회로망의 성능에 중요한 영향을 미친다. 본 논문은 데이터와 클래스들의 대표값들 사이의 거리를 고려하여 학습률을 정하는 새로운 퍼지 학습법칙을 제안한다. 클래스들의 대표값을 조정할 때, 이러한 고려는 outlier에 비하여 결정경계선 근처에 있는 데이터의 반영도를 높임으로써 outlier의 클래스의 대표값에 미치는 영향도를 낮출 수 있다. 따라서 outlier들이 결정경계선을 악화시키는 것을 방지할 수 있다. 이 새로운 퍼지 학습법칙을 IAFC(Integrated Adaptive Fuzzy Clustering) 신경회로망에 적용하였다. 제안한 퍼지 신경회로망과 다른 감독 신경회로망들의 성능을 비교하기 위하여 iris 데이터를 사용하였다. iris 데이터를 사용하여 테스트한 결과 제안한 퍼지 신경회로망의 성능이 우수함을 보였다.

Keywords

References

  1. S. Haykin, 'Neural Networks-A Comprehensive Foundation 2nd Ed,' Prentice Hall, Upper Saddle River, NJ, 1999
  2. T. Kohonen, 'Learning Vector Quantization for Pattern Recognition,' Technical Report TKK-F-A60l, Helsinki University of Technology, Filand, 1986
  3. J. C. Bezdek, E. C. Tsao, and N. R. Pal, 'Fuzzy Kohonen Clustering Networks,' Proceedings of the First IEEE Conference on Fuzzy Systems, San Diego pp. 1035-1043, 1992
  4. F. -L. Chung and T. Lee, 'A Fuzzy Learning Model for Membership Function Estimation and Pattern classification,' Proceedings of the IEEE Conference on Fuzzy Systems, Vol. 1, pp. 426-431, 1994
  5. F. -L. Chung and T. Lee, 'Fuzzy Learning Vector Quantization,' Proceedings of 1993 International Joint Conference on Neural Networks, Nagoya, Vol. 3, pp. 2739-2743, 1993
  6. N. B. Karayiannis, 'Weighted Fuzzy Learning Vector Quantization and Weighted Fuzzy c-Means Algorithms,' IEEE International Conference on Neural Networks, Vol. 2, pp. 1044-1049, 1996
  7. N. B. Karayiannis and J. C. Bezdek, 'An Integrated Approach to Fuzzy Learning Vector Quantization and Fuzzy c-Means,' IEEE Transactions on Fuzzy Systems, Vol. 5, pp. 662-629, 1997
  8. E. C. -K. Tsao, J. C. Bezdek, and N. R. Pal, 'Fuzzy Kohonen Clustering Networks,' Pattern Recognition, Vol. 27, No.5, pp. 757-764, 1994 https://doi.org/10.1016/0031-3203(94)90052-3
  9. G. A. Carpenter S. Grossberg, 'A Massively Parallel Architecture for A Self- Organizing Neural Pattern Recognition Machine,' Computer Vision, Graphics, and Image Processing, Vol. 37, pp. 54-115, 1987 https://doi.org/10.1016/S0734-189X(87)80014-2