The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Design of Granular-based Neurocomputing Networks for Modeling of Linear-Type Superconducting Power Supply

리니어형 초전도 전원장치 모델링을 위한 입자화 기반 Neurocomputing 네트워크 설계

  • 박호성 (수원대 산업기술연구소) ;
  • 정윤도 (수원대 산업기술연구소) ;
  • 김현기 (수원대 공대 전기공학과) ;
  • 오성권 (수원대 공대 전기공학과)
  • Received : 2009.10.09
  • Accepted : 2010.04.20
  • Published : 2010.07.01
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Abstract

In this paper, we develop a design methodology of granular-based neurocomputing networks realized with the aid of the clustering techniques. The objective of this paper is modeling and evaluation of approximation and generalization capability of the Linear-Type Superconducting Power Supply (LTSPS). In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The underlying design tool guiding the development of the granular-based neurocomputing networks revolves around the Fuzzy C-Means (FCM) clustering method and the Radial Basis Function (RBF) neural network. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the membership values of the input space with the aid of FCM clustering. To modeling and evaluation of performance of the linear-type superconducting power supply using the proposed network, we describe a detailed characteristic of the proposed model using a well-known NASA software project data.

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References

  1. Y. D. Chung, I. Muta, T. Hoshino, and T. Nakamura, "Characteristics of a Persistent Current Compensator for Superconducting NMR Magnets Using Linear Type Magnetic Flux Pump," IEEE Trans. Applied Superconductivity, Vol. 15, No. 2, pp. 1338-1341, 2006.
  2. Y. D. Chung, T. Hoshino, and T. Nakamura, "Current Pumping Performance of Linear-Type Magnetic Flux Pump With Use of Feedback Control Circuit System," IEEE Trans. Applied Superconductivity, Vol. 16, No. 2, pp. 1638-1641, 2006.
  3. W. Pedrycz and A. T. Vasilakos, "Computational Intelligence in Telecommunications Networks," CRC Press, 2000.
  4. D. Srinivasan, C. W. Chan, and P. G. Balaji, "Computational intelligence-based congestion prediction for a dynamic urban street network," Neurocomputing, Vol. 72 pp. 2710-2716, 2009. https://doi.org/10.1016/j.neucom.2009.01.005
  5. G. A. Montazer, R. Sabzevari, H. G. Khatir, "Improvement of learning algorithms for RBF neural networks in a helicopter sound identification system," Neurocomputing, Vol. 71, pp. 167-173, 2007. https://doi.org/10.1016/j.neucom.2007.08.002
  6. J. S. Lee, R. Sankar, "Theoretical derivation of minimum mean square error of RBF based equalizer," Signal Processing, Vol. 87, pp. 1613-1625, 2007. https://doi.org/10.1016/j.sigpro.2007.01.008
  7. K. B. Kim and S. S. Kim, "A passport recognition and face verification using enhanced fuzzy ART based RBF network and PCA algorithm," Neurocomputing, Vol. 71, pp. 3202-3210, 2008. https://doi.org/10.1016/j.neucom.2008.04.045
  8. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, 1981.
  9. I. Myrtveit, E. Stensrud, and M. Shepperd, "Reliability and validity in comparative studies of software prediction models," IEEE Trans. on Software Engineering, Vol. 31, No. 5, pp. 380-391, 2005. https://doi.org/10.1109/TSE.2005.58
  10. I. Witten and E. Frank, Data mining: Practical machine learning tools and techniques (2nd ed.), Morgan Kaufmann, San Francisco (2005).
  11. M. Shin and A. Goel, "Empirical data modeling in software engineering using radial basis functions," IEEE Trans. Software Engineering, Vol. 26, No. 6, pp. 567-576, 2000. https://doi.org/10.1109/32.852743
  12. A. Oliveira, "Estimation of software project effort with support vector regression," Neurocomputing, Vol. 69, pp. 1749-1753, 2006. https://doi.org/10.1016/j.neucom.2005.12.119
  13. P. Singla, K. Subbarao, and J. L. Junkins, "Direction-dependent learning approach for radial basis function networks," IEEE Trans. Neural Networks, Vol. 18, No. 1, pp. 203-222, 2007. https://doi.org/10.1109/TNN.2006.881805
  14. A. Alexandridis, H. Sarimveis, and G. Bafas, "A new algorithm for online structure and parameter adaptation of RBF networks," Neural Networks, Vol. 16, pp. 1003-1017, 2003. https://doi.org/10.1016/S0893-6080(03)00052-2
  15. X. Hong, "A fast identification algorithm for Box– Cox transformation based radial basis function neural network," IEEE Trans. Neural Networks, Vol. 17, No.4, pp. 1064-1069, 2006. https://doi.org/10.1109/TNN.2006.875986
  16. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning, MIT Press, 2006.
  17. http://www.mathworks.com/access/helpdesk/help/toolbox/nnet.