Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지

Optimal Design of Radial Basis Function Network Using Time-Frequency Localization

시간-주파수 지역화를 이용한 방사 기준 함수 구조의 최적 설계

  • Kim, Yong-Taek (School of Electrical and Electronic Eng., Chung-Ang University) ;
  • Kim, Seong-Joo (School of Electrical and Electronic Eng., Chung-Ang University) ;
  • Seo, Jae-Yong (Korea University of Technology and Education) ;
  • Jeon, Hong-Tae (School of Electrical and Electronic Eng., Chung-Ang University)
  • 김용택 (중앙대학교 전자전기공학부) ;
  • 김성주 (중앙대학교 전자전기공학부) ;
  • 서재용 (한국기술교육대학교) ;
  • 전홍태 (중앙대학교 전자전기공학부)
  • Published : 2001.10.30
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose the initial optimized structure of the Radial Basis Function Network(RBFN) which is more simple in the part of the structure and converges more faster than Neural Network. For this, we use the analysis method using time frequency localization and we can decide the initial structure of the RBFN suitable for the given problem. When we compose the hidden nodes with the radial basis functions whose localization are similar with the target function in the plane of the time and frequency, we can make a good decision of the initial structure having an ability of approximation.

본 논문에서는 신경망에 비해 보다 단순화되고 빠르게 수렴하는 특성을 보이는 방사 기준 함수 구조를 초기에 설계하기 위한 방법을 제안한다. 이를 위해 시간 주파수 지역화를 이용한 분석 기법을 사용하였고 방사기준 함수 구조를 초기에 주어진 문제에 적합한 최적 상태로 결정하였다. 시간-주파수 평면에서 지역화 특성이 대상 함수를 근사할 수 있는 특성을 지닌 방사 기준 함수를 사용하여 은닉층을 구성할 경우, 근사 능력을 지닌 초기 구조를 결정함에 있어서 장점을 지닌다.

Keywords

References

  1. Chen, S., C. F. N. Cowan, and P. M Crant., 'Orthogonal least squares learning algorithm forl radial basis function networks,' IEEE Trans. Neural Network', Vol.2, No.2, pp.302-300, Mar. 1991 https://doi.org/10.1109/72.80341
  2. V. T. Sunil Elanayar and Y. C. Shin, 'Radial Basis Function Neural Network for Approximation and Estimation of Nonlinear Stochastic Dynamic Systems,' IEEE Trans, on Neural Networks, Vol. 5, No. 4,pp. 594-603, Jul. 1994 https://doi.org/10.1109/72.298229
  3. Xiao D. Ji and Babajide O. Familoni, 'A Diagonal Recurrent Neural Network Based Hybrid Direct Adaptive SPSA Control System,' IEEE Transactions on Automatic Control, Vol. 44, No.7, pp.1469-1473, July 1999 https://doi.org/10.1109/9.774125
  4. A. Karakasoglu, S.I. Sudharsanan, and M. K. Sundareshan, 'Identification and decentralized adaptive control using dynamic neural networks with application to robotic manipulators,' IEEE Transactions on Neural Networks, Vol. 4, No. 6, pp. 919-930, November 1999 https://doi.org/10.1109/72.286887
  5. H. Dym and H. P. McKean., Fourier Series and Integrals, Academic Press, San Diego, 1972
  6. Mark J. L. Orr, Introduction to Radial Basis Function Networks, Center for Cognitive Science, Scotland, 1996