Journal of the Institute of Electronics Engineers of Korea TE (대한전자공학회논문지TE)

The Institute of Electronics and Information Engineers (대한전자공학회)
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A Study on Fuzzy Wavelet Neural Network System Based on ANFIS Applying Bell Type Fuzzy Membership Function

벨형 퍼지 소속함수를 적용한 ANFIS 기반 퍼지 웨이브렛 신경망 시스템의 연구

  • 변오성 (원광대학교 전자공학과) ;
  • 조수형 (순천제일대학 전자정보통신학부) ;
  • 문성용 (원광대학교 전기ㆍ전자 및 정보공학부)
  • Published : 2002.12.01
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Abstract

In this paper, it could improved on the arbitrary nonlinear function learning approximation which have the wavelet neural network based on Adaptive Neuro-Fuzzy Inference System(ANFIS) and the multi-resolution Analysis(MRA) of the wavelet transform. ANFIS structure is composed of a bell type fuzzy membership function, and the wavelet neural network structure become composed of the forward algorithm and the backpropagation neural network algorithm. This wavelet composition has a single size, and it is used the backpropagation algorithm for learning of the wavelet neural network based on ANFIS. It is confirmed to be improved the wavelet base number decrease and the convergence speed performances of the wavelet neural network based on ANFIS Model which is using the wavelet translation parameter learning and bell type membership function of ANFIS than the conventional algorithm from 1 dimension and 2 dimension functions.

본 논문은 적응성 뉴로-퍼지 인터페이스 시스템(Adaptive Neuro-Fuzzy Inference System : ANFIS)과 웨이브렛 변환 다중해상도 분해(multi-resolution Analysis : MRA)을 기반으로 한 웨이브렛 신경망을 가지고 임의의 비선형 함수 학습 근사화를 개선하는 것이다. ANFIS 구조는 벨형 퍼지 소속 함수로 구성이 되었으며, 웨이브렛 신경망은 전파 알고리즘과 역전파 신경망 알고리즘으로 구성되었다. 이 웨이브렛 구성은 단일 크기이고, ANFIS 기반 웨이브렛 신경망의 학습을 위해 역전파 알고리즘을 사용하였다. 1차원과 2차원 함수에서 웨이브렛 전달 파라미터 학습과 ANFIS의 벨형 소속 함수를 이용한 ANFIS 모델 기반 웨이브렛 신경망의 웨이브렛 기저 수 감소와 수렴 속도 성능이 기존의 알고리즘 보다 개선되었음을 확인하였다.

Keywords

References

  1. G. Cybenko, 'Approximation by superposition of a sigmoidal function,' Mathematics of control, signals and systems, Vol. 2, pp. 303-314, 1989
  2. L. K. Jones, 'Constructive approximations for neural networks by sigmoidal function,' Proc. IEEE, Vol. 78, Oct. 1990
  3. T. Poggio and F. Girosi, 'Networks for approximation and learning,' Proc. IEEE, Vol. 78, pp. 1481-1497, Sept. 1990 https://doi.org/10.1109/5.58326
  4. I. Daubechies, 'The wavelet transform, timefrequency localization and signal analysis,' IEEE Trans. Informat. Theory, Vol. 36, Sept. 1990
  5. J. J. Benedetto and M. W. Frazier, 'Wavelets Mathematics and Applications,' CRC Press, Inc., 1994
  6. C. S. Burrus, R. A. Gopinath and H. Guo, 'Introduction to Wavelets and Wavelet Transforms,' Prentice-Hall International, Inc., 1998
  7. Y. Y. Tang, L. H. Yang, and J. Liu, and H. Ma, 'Wavelet Theory and its Application to Pattern Recognition,' World Scientific Publishing Co. Pte. Ltd, 2000
  8. Q. Zhang and A. Benveniste, 'Wavelet networks,' IEEE Trans. Neural Networks, Vol. 3, pp. 889-898, Nov. 1992 https://doi.org/10.1109/72.165591
  9. B. Delyon, A. Juditsky, and A. Benveniste, 'Accuracy Analysis for Wavelet Approximations,' IEEE Trans. Neural Networks, Vol. 6, No. 2, pp. 332-348, Mar. 1995 https://doi.org/10.1109/72.363469
  10. J. Zhang, G. G. Walter, and W. N. Wayne Lee, 'Wavelet Neural Networks for Function Learning,' IEEE Trans. signal Processing, Vol. 43, No. 6, pp. 1485-1496, Jun. 1995 https://doi.org/10.1109/78.388860
  11. Q. Zhang, 'Using Wavelet Network in Nonparametric Estimation,' IEEE Trans. Neural Networks, Vol. 8, No. 2, pp. 227-236, Mar. 1997 https://doi.org/10.1109/72.557660
  12. J.-S. R. Jang, C. T. Sun, and E. Mizutani, 'Neuro-Fuzzy and Soft Computing,' Prentice-Hall International, Inc.,1997