About this Journal Submit a Manuscript Table of Contents
Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 790526, 11 pages
http://dx.doi.org/10.1155/2012/790526
Research Article

Electrical Drive Radiated Emissions Estimation in Terms of Input Control Using Extreme Learning Machines

Electronics Department, Polytechnics School, University of Alcalá, Campus Universitario 28871, Alcalá de Henares, Spain

Received 21 September 2012; Revised 9 November 2012; Accepted 9 November 2012

Academic Editor: Wuhong Wang

Copyright © 2012 A. Wefky et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Ben Hadj Slama, S. Hrigua, F. Costa, B. Revol, and C. Gautier, “Relevant parameters of SPICE3 MOSFET model for EMC analysis,” in Proceedings of the IEEE International Symposium on Electromagnetic Compatibility (EMC '09), pp. 319–323, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Chen, T. W. Nehl, J. S. Lai et al., “Towards EMI prediction of a PM motor drive for automotive applications,” in Proceedings of the 18th Annual IEEE Applied Power Electronics Conference and Exposition (APEC '03), pp. 14–22, February 2003. View at Scopus
  3. L. Ferrer, J. Balcells, D. González, J. Gago, and M. Lamich, “Modelling of differential mode conducted EMI generated by switched power inverters,” in Proceedings of the 29th Annual Conference of the IEEE Industrial Electronics Society (IECON '03), pp. 2312–2315, November 2003. View at Publisher · View at Google Scholar · View at Scopus
  4. R. Kahoul, Y. Azzouz, P. Marchal, and B. Mazari, “New behavioral modeling for DC motor armatures applied to automotive EMC characterization,” IEEE Transactions on Electromagnetic Compatibility, vol. 52, no. 4, pp. 888–901, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. W. Valente, M. H. Amaral, and A. Raizer, “EMC management: how to compare electromagnetic environmental measurements and equipment immunity levels,” Progress In Electromagnetics Research Letters, vol. 18, pp. 165–177, 2010. View at Scopus
  6. J. L. Guardado, J. A. Flores, V. Venegas, J. L. Naredo, and F. A. Uribe, “A machine winding model for switching transient studies using network synthesis,” IEEE Transactions on Energy Conversion, vol. 20, no. 2, pp. 322–328, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. M. K. Kazimierczuk, G. Sancineto, G. Grandi, U. Reggiani, and A. Massarini, “High-frequency small-signal model of ferrite core inductors,” IEEE Transactions on Magnetics, vol. 35, no. 5, pp. 4185–4191, 1999. View at Scopus
  8. S. Moreau, R. Kahoul, and J. P. Louis, “Parameters estimation of permanent magnetsynchronous machine without adding extra-signal input excitation,” in Proceedings of the IEEE International Symposium on Industrial Electronics (IEEE-ISlE '04), pp. 371–376, May 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Kahoul, Y. Azzouz, and B. Ravelo, “Modelling of DC motors conducted low frequency EMI/EMC disturbance for automotive applications,” European Journal of Scientific Research, vol. 63, no. 3, pp. 368–386, 2011.
  10. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  11. Y. Xu, Z. Y. Dong, J. H. Zhao, P. Zhang, and K. P. Wong, “A reliable intelligent system for real-time dynamic security assessment of power systems,” IEEE Transactions on Power Systems, vol. 27, no. 3, Article ID Article number6158623, pp. 1253–1263, 2012. View at Publisher · View at Google Scholar
  12. S. Samet and A. Miri, “Privacy-preserving back-propagation and extreme learning machine algorithms,” Data and Knowledge Engineering, vol. 79-80, pp. 40–61, 2012. View at Publisher · View at Google Scholar
  13. Y. Song, J. Crowcroft, and J. Zhang, “Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine,” Journal of Neuroscience Methods, vol. 210, no. 2, pp. 132–146, 2012. View at Publisher · View at Google Scholar
  14. S. Decherchi, P. Gastaldo, R. Zunino, E. Cambria, and J. Redi, “Circular-ELM for the reduced-reference assessment of perceived image quality,” Neurocomputing, vol. 102, pp. 78–89, 2013. View at Publisher · View at Google Scholar
  15. S. Decherchi, P. Gastaldo, A. Leoncini, and R. Zunino, “Efficient digital implementation of extreme learning machines for classification,” IEEE Transactions on Circuits and Systems II, vol. 59, no. 8, Article ID Article number6236105, pp. 496–500, 2012. View at Publisher · View at Google Scholar
  16. K. Choi, K.-A. Toh, and H. Byun, “Incremental face recognition for large-scale social network services,” Pattern Recognition, vol. 45, no. 8, pp. 2868–2883, 2012. View at Publisher · View at Google Scholar
  17. R. Minhas, A. A. Mohammed, and Q. M. Wu, “Incremental learning in human action recognition based on snippets,” IEEE Transactions on Circuits and Systems for Video Technology, vol. PP, no. 99, 1 pages, 2011.
  18. G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme learning machine for regression and multiclass classification,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 42, no. 2, Article ID 6035797, pp. 513–529, 2012. View at Publisher · View at Google Scholar
  19. F. Espinosa, J. A. Jiménez, E. Santiso, et al., “Design and implementation of a portable electronic system for vehicle-driver-route activity measurement,” Measurement, vol. 44, no. 2, pp. 326–337, 2011. View at Publisher · View at Google Scholar
  20. Commission, I. E., CISPR 16-2-3 “Specification for radio disturbance and immunity measuring apparatus and methods Part 2-3: methods of measurement of disturbances and immunity—radiated disturbance measurements”, 2008.
  21. C. Miyajima, Y. Nishiwaki, K. Ozawa, T. Wakita, K. Itou, and K. Takeda, “Cepstral analysis of driving behavioral signals for driver identification,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '06), pp. V921–V924, May 2006. View at Scopus
  22. W. Wang, W. Zhang, H. Guo, H. Bubb, and K. Ikeuchi, “A safety-based approaching behavioural model with various driving characteristics,” Transportation Research Part C, vol. 19, no. 6, pp. 1202–1214, 2011. View at Publisher · View at Google Scholar
  23. S. Alexandersson and M. Alaküla, “Automotive power electronic future–from an EMC perspective,” in Proceedings of the International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM '06), pp. 609–613, May 2006. View at Publisher · View at Google Scholar · View at Scopus
  24. L. De Santiago, F. Espinosa, M. A. Ruiz et al., “Effect of electrical vehicle-driver interaction on the radiated electromagnetic emissions: measurement methodology,” in Proceedings of the IEEE-ICIT International Conference on Industrial Technology (ICIT '10), pp. 1113–1118, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. C. Keller and K. Feser, “Fast emission measurement in time domain,” IEEE Transactions on Electromagnetic Compatibility, vol. 49, no. 4, pp. 816–824, 2007. View at Publisher · View at Google Scholar · View at Scopus
  26. C. Keller and K. Feser, “A new method of emission measurement,” in Proceedings of the IEEE International Symposium on Electromagnetic Compatibility (EMC '02), pp. 599–604, August 2002. View at Scopus
  27. C. Keller and K. Feser, “Fast emission measurement in time domain,” in Proceedings of the 14th International Zurich Symposium On Electromagnetic Compatibility (EMC '01), Zurich, Switzerland, 2001.
  28. F. Krug, D. Mueller, and P. Russer, “Signal processing strategies with the TDEMI measurement system,” IEEE Transactions on Instrumentation and Measurement, vol. 53, no. 5, pp. 1402–1408, 2004. View at Publisher · View at Google Scholar · View at Scopus
  29. G. B. Huang, D. H. Wang, and Y. Lan, “Extreme learning machines: a survey,” International Journal of Machine Learning and Cybernetics, vol. 2, no. 2, pp. 107–122, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. G. B. Huang, L. Chen, and C. K. Siew, “Universal approximation using incremental constructive feedforward networks with random hidden nodes,” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879–892, 2006. View at Publisher · View at Google Scholar · View at Scopus
  31. S. S. Haykin, Neural Networks: A Comprehensive foundation, Prentice Hall, 1999.
  32. M. T. Hagan, Neural Network Design, Brooks/Cole, 1996.
  33. C. M. Bishop, Neural Networks for Pattern Recognition, The Clarendon Press Oxford University Press, New York, NY, USA, 1995. View at Zentralblatt MATH