Browse > Article
http://dx.doi.org/10.5391/JKIIS.2010.20.1.140

Nonlinear Characteristic Analysis of Charging Current for Linear Type Magnetic Flux Pump Using RBFNN  

Chung, Yoon-Do (수원대학교 전기공학과)
Park, Ho-Sung (수원대학교 전기공학과)
Kim, Hyun-Ki (수원대학교 전기공학과)
Oh, Sung-Kwun (수원대학교 전기공학과)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.20, no.1, 2010 , pp. 140-145 More about this Journal
Abstract
In this work, to theoretically analyze the nonlinear charging characteristic, a Radial Basis Function Neural Network (RBFNN) is adopted. Based on the RBFNN, an charging characteristic tendency of a Linear Type Magnetic Flux Pump (LTMFP) is analyzed. In the paper, we developed the LTMFP that generates stable and controllable charging current and also experimentally investigated its charging characteristic in the cryogenic system. From these experimental results, the charging current of the LTMFP was also found to be frequency dependent with nonlinear quality due to the nonlinear magnetic behaviour of superconducting Nb foil. On the whole, in the case of essentially cryogenic experiment, since cooling costs loomed large in the cryogenic environment, it is difficult to carry out various experiments. Consequentially, in this paper, we estimated the nonlinear characteristic of charging current as well as realized the intelligent model via the design of RBFNN based on the experimental data. In this paper, we view RBF neural networks as predominantly data driven constructs whose processing is based upon an effective usage of experimental data through a prudent process of Fuzzy C-Means clustering method. Also, the receptive fields of the proposed RBF neural network are formed by the FCM clustering.
Keywords
Superconducting flux pump; RBF neural network; FCM clustering method;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Markiewicz WD, Miller JR, Schwartz J, Trociewitz UP, Weijers HW, "Prospective on a superconducting 30 T/1.3 GHz NMR spectrometer magnet," IEEE Transactions on Applied Superconductivity, vol. 16, no. 2, pp. 1523-1526, 2006.   DOI
2 Y. Iwasa, "Microampere flux pump for superconducting NMR magnets Part1; basic concept and microtesla flux measurement," Cryogenics, vol. 41, pp. 385-391, 2001.   DOI
3 Yoondo Chung, Itsuya Muta, Tsutomu Hoshino, Taketsune Nakamura and M.H. Sohn, "Design and performance of compensator for decremental persistent current in HTS magnets using linear type magnetic flux pump," Cryogenics, vol. 44, no. 11, pp. 839-844, Nov. 2004.   DOI
4 M. Han and J. Xi, "Efficient clustering of radial basis perceptron neural network for pattern recognition," Pattern Recognition, vol. 37, pp. 2059-2067, 2004.   DOI
5 J. M. Vilaplana, J. L. P. Molina, and J. L. Coronado, "Hyper RBF model for accurate reaching in redundant robotic systems," Neurocomputing, vol. 61, pp. 495-501, 2004.   DOI
6 M. Wallace, N. Tsapatsoulis, and S. Kollias, "Intelligent initialization of resource allocating RBF networks," Neural Networks, vol. 18, pp. 117-122, 2005.   DOI
7 H.-S. Park, W. Pedrycz and S.-K. Oh, "Granular Neural Networks and Their Development Through Context-Based Clustering and Adjustable Dimensionality of Receptive Fields", IEEE Trans. on Neural Networks, vol. 20, no. 10, pp. 1604-1616, 2009.   DOI   ScienceOn