Browse > Article

A Study on Fuzzy Wavelet Neural Network System Based on ANFIS Applying Bell Type Fuzzy Membership Function  

변오성 (원광대학교 전자공학과)
조수형 (순천제일대학 전자정보통신학부)
문성용 (원광대학교 전기ㆍ전자 및 정보공학부)
Publication Information
Journal of the Institute of Electronics Engineers of Korea TE / v.39, no.4, 2002 , pp. 363-369 More about this Journal
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.
Keywords
ANFIS; backpropagation; fuzzy membership function;
Citations & Related Records
연도 인용수 순위
  • Reference
1 L. K. Jones, 'Constructive approximations for neural networks by sigmoidal function,' Proc. IEEE, Vol. 78, Oct. 1990
2 J. J. Benedetto and M. W. Frazier, 'Wavelets Mathematics and Applications,' CRC Press, Inc., 1994
3 C. S. Burrus, R. A. Gopinath and H. Guo, 'Introduction to Wavelets and Wavelet Transforms,' Prentice-Hall International, Inc., 1998
4 Q. Zhang, 'Using Wavelet Network in Nonparametric Estimation,' IEEE Trans. Neural Networks, Vol. 8, No. 2, pp. 227-236, Mar. 1997   DOI   ScienceOn
5 Q. Zhang and A. Benveniste, 'Wavelet networks,' IEEE Trans. Neural Networks, Vol. 3, pp. 889-898, Nov. 1992   DOI   ScienceOn
6 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   DOI   ScienceOn
7 J.-S. R. Jang, C. T. Sun, and E. Mizutani, 'Neuro-Fuzzy and Soft Computing,' Prentice-Hall International, Inc.,1997
8 I. Daubechies, 'The wavelet transform, timefrequency localization and signal analysis,' IEEE Trans. Informat. Theory, Vol. 36, Sept. 1990
9 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
10 G. Cybenko, 'Approximation by superposition of a sigmoidal function,' Mathematics of control, signals and systems, Vol. 2, pp. 303-314, 1989
11 T. Poggio and F. Girosi, 'Networks for approximation and learning,' Proc. IEEE, Vol. 78, pp. 1481-1497, Sept. 1990   DOI   ScienceOn
12 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   DOI   ScienceOn