Table of Contents
Advances in Artificial Neural Systems
Volume 2010 (2010), Article ID 984381, 11 pages
http://dx.doi.org/10.1155/2010/984381
Research Article

Determination of Complex-Valued Parametric Model Coefficients Using Artificial Neural Network Technique

Department of Mechatronics, International Islamic University Malaysia (IIUM), P.O. Box 10, 53100 Gombak, Malaysia

Received 10 April 2009; Accepted 8 March 2010

Academic Editor: Yehoshua Zeevi

Copyright © 2010 A. M. Aibinu 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. E. N. Bruce, Biomedical Signal Processing and Signal Modeling, Wiley Interscience, New York, NY, USA, 2000.
  2. S. L. Marple, Digital Spectral Analysis with Applications, Prentice Hall, Upper Saddle River, NJ, USA, 1987.
  3. M. H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley & Sons, New York, NY, USA, 1996.
  4. J. G. Proakis and D. G. Manolakis, Digital Signal Processing: Principles, Algorithms and Applications, Prentice Hall, Upper Saddle River, NJ, USA, 4th edition, 2007.
  5. D. G. Manolakis, V. K. Ingle, and S. M. Kogon, Statistical and Adaptive Signal Processing, McGraw Hill, New York, NY, USA, 2000.
  6. K. H. Chon and R. J. Cohen, “Linear and nonlinear ARMA model parameter estimation using an artificial neurol network,” IEEE Transactions on Biomedical Engineering, vol. 44, no. 3, pp. 168–174, 1997. View at Google Scholar · View at Scopus
  7. K. H. Chon, D. Hoyer, A. A. Armoundas, N.-H. Holstein-Rathlou, and D. J. Marsh, “Robust nonlinear autoregressive moving average model parameter estimation using recurrent artificial neural network,” Annals of BioMedical Engineering, vol. 27, pp. 538–547, 1999. View at Google Scholar
  8. A. M. Aibinu, M. Nilsson, M. J. E. Salami, and A. A. Shafie, “A new method of voice activity detection using real-valued neural network based autoregressive modeling technique,” submitted for publication in Computer in Biology and Medicine.
  9. M. R. Smith, S. T. Nichols, R. T. Constable, and R. M. Henkelman, “A quantitative comparison of the TERA modeling and DFT magnetic resonance image reconstruction techniques,” Magnetic Resonance in Medicine, vol. 19, no. 1, pp. 1–19, 1991. View at Google Scholar · View at Scopus
  10. Z. P. Liang and P. C. Lauterbur, Principles of Magnetic Resonance Imaging: A Signal Processing Perspective, IEEE Press, New York, NY, USA, 2000.
  11. M. R. Smith, S. T. Nichols, R. M. Henkelman, and M. L. Wood, “Application of autoregressive moving average parametric modeling in magnetic resonance image reconstruction,” IEEE Transactions on Medical Imaging, vol. 1–5, no. 3, pp. 257–261, 1986. View at Google Scholar
  12. M. J. Salami, A. R. Najeeb, O. Khalifa, and K. Arrifin, “MR Reconsturction with autoregressive moving average,” in International Conference on Biotechnology Engineering, pp. 676–704, Kuala Lumpur, Malaysia, May 2007.
  13. M. Aibinu, A. A. Shafie, M. J. E. Salami, A. F. Salami, and W. A. Lawal, “Development of a new method of crack modeling and prediction algorithm,” in Proceedings of the 3rd International Conference on Mechatronics (ICOM '08), pp. 434–438, Kuala Lumpur, Malaysia, December 2008.
  14. W. J. Kim, S. H. Chang, and B. H. Lee, “Application of neural networks to signal prediction in nuclear power plant,” IEEE Transactions on Nuclear Science, vol. 40, no. 5, pp. 1337–1341, 1993. View at Publisher · View at Google Scholar · View at Scopus
  15. S. A. Fattah, W.-P. Zhu, and M. O. Ahmad, “An algorithm for the identification of autoregressive moving average systems from noisy observations,” in Proceedings of the Canadian Conference on Electrical and Computer Engineering, pp. 1815–1818, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. G. Liang, M. Wilkes, and J. A. Cadzow, “ARMA model order estimation based on the eigenvalues of the covariance matrix,” IEEE Transactions on Signal Processing, vol. 41, no. 10, 1993. View at Google Scholar
  17. B. Widrow and S. D. Stearns, Adaptive Signal Processing, Prentice Hall, Englewood Cliffs, NJ, USA, 1985.
  18. S. M. Kay and S. L. Marple, “Spectrum analysis A modern perspectiv,” Proceedings of the IEEE, vol. 69, no. 11, pp. 1380–1419, 1981. View at Google Scholar
  19. M. B. Priestley, Spectral Analysis and Time Series, Academic Press, London, UK, 1981.
  20. S. M. Kay, Modern Spectral Estimation: Theory and Application, Prentice-Hall, Englewood Cliffs, NJ, USA, 1988.
  21. J. Makhoul, “Linear prediction, a tutorial,” Proceedings of the IEEE, vol. 63, no. 4, pp. 561–580, 1975. View at Google Scholar · View at Scopus
  22. S. Lu and K. H. Chon, “A new algorithm for ARMA model parameter estimation using group method of data handling,” in Proceedings of the IEEE 26th Annual Northeast Bioengineering Conference, pp. 127–128, Storrs, Conn, USA, 2000.
  23. G. Blanchet and M. Charbut, “Digital Signal and Image Processing using MATLAB,” HERMES UK, 2006.
  24. P. M. T. Broersen, “Modified Durbin method for accurate estimation of moving-average models,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 5, pp. 1361–1369, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Hirose, “Complex-valued neural networks,” in Series on Studies in Computational Intelligence, p. 176, Springer, New York, NY, USA, 2006. View at Google Scholar
  26. A. Hirose, Ed., Complex-Valued Neural Networks: Theories and Applications, Series on Innovative Intelligence, World Scientific, Singapore, 2003.
  27. A. Hirose, “Complex-valued neural networks for more fertile electronics,” Journal of the IEICE, vol. 87, no. 6, pp. 447–449, 2004 (Japanese). View at Google Scholar
  28. A. Hirose, “The “super brain” and the complex-valued neural networks,” Suuri-Kagaku, Saiensu-sha, no. 492, p. 7883, 2004 (Japanese). View at Google Scholar
  29. A. Hirose, “Complex-valued neural networks: the merits and their origins,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '09), pp. 1237–1244, Atlanta, Ga, USA, June 2009. View at Publisher · View at Google Scholar · View at Scopus
  30. Y. H. Hu and J. Hwang, Introduction to Neural Networks for Signal Processing, CRC Press LLC, Boca Raton, Fla, USA, 2002.
  31. A. M. Aibinu, R. F. Olanrewaju, M. J. E. Salami, and A. Abdallah-Hassan, “Biological data classification using complex and real valued neural network,” in Proceedings of the International Conference on Data Analysis, Data Quality and Metadata Management (DAMD '10), 2010.
  32. A. M. Aibinu, M. J. E. Salami, and A. A. Shafie, “Complex Valued Autoregressive Modleing Technique Using Artificial Neural Network,” Biosignal Interpretation, Yale, USA, 2009.
  33. Y. Hui and M. R. Smith, “MRI reconstruction from truncated data using a complex domain backpropagation neural network,” in Proceedings of the IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing, pp. 513–516, Victoria, BC, Canada, 1995. View at Scopus
  34. M. R. Smith and Y. Hui, “A data extrapolation algorithm using a complex domain neural network,” IEEE Transactions on Circuits and Systems II, vol. 44, no. 2, pp. 143–147, 1997. View at Google Scholar · View at Scopus
  35. Y. Hui and M. R. Smith, “Comment on data truncalion artifact reduction in MR imaging using a multilayer neural networks,” IEEE Transactions on Medical Imaging, 1995. View at Google Scholar
  36. R. L. Kashyap, “Inconsitency in AIC rule for estimating the order of autoregressive models,” IEEE Transactions on Automatic Control, vol. 25, pp. 996–998, 1980. View at Google Scholar
  37. R. Palaniappan, “Towards optimal model oreder selection for autoregressive spectral analysis of mental tasks using genetic a lgorithm,” International Journal of Computer Science and Network Security, vol. 6, no. 1A, 2006. View at Google Scholar
  38. R. J. Watson, C. C. McLean, M. P. Moore et al., “Classification of arterial plaque by spectral analysis of in vitro radio frequency intravascular ultrasound data,” Ultrasound in Medicine and Biology, vol. 26, p. 7380, 2000. View at Google Scholar
  39. S. Haykin and S. Kesler, “Prediction-error filtering and maximum-entropy spectral estimation,” Topics in Applied Physic, vol. 34, pp. 9–72, 1983. View at Google Scholar
  40. J. Burg, “A New Analysis Technque for Time Series Data,” NATO Advanced Study Institute On Signal Processing eith Emphasis on Underwater Acoustics, August 1968.
  41. H. Akaik, “Power spectrum estimation through autoregression model fitting,” Annals of the Institute of Statistical Mathematics, vol. 21, pp. 407–419, 1969. View at Google Scholar
  42. A. M. Aibinu, A. R. Najeeb, A. A. Shafie, and M. J. E. Salami, “Optimal model order selection for parametric magnetic resonance image reconstruction,” in Proceedings of the International Conference on Medical System Engineering (ICMSE), vol. 32, pp. 191–195, August-September 2008.
  43. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, Eaglewood, Cliffs, NJ, USA, 2nd edition, 1998.
  44. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. View at Google Scholar · View at Scopus
  45. G. A. Tagliarini, “An Introduction To The Backpropagation Algorithm,” Lecture note, 2001.
  46. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing, Foundations, Ed., vol. 1, pp. 318–362, MIT Press, 1986. View at Google Scholar
  47. H. Hsin, C. Li, M. Sun, and R. J. Sclabassi, “Adaptive training algorithm for back-propagation neural networks,” IEEE Transactions on Systems, Man and Cybernetics, pp. 1049–1052, 1992. View at Google Scholar
  48. T. P. Vogl, J. K. Mangis, W. T. Zink, A. K. Rigler, and D. L. Alkon, “Accelerating the convergence of the back propagation method,” Biology Cybernetics, vol. 59, pp. 257–263, 1988. View at Google Scholar
  49. G. Cao, “Comments on can backpropagation error surface not have local minima,” Private Communication, 1993. View at Google Scholar
  50. R. A. Jacobs, “Increased rates of convergence through learning rate adaptation,” Neural Networks, vol. 1, no. 4, pp. 295–307, 1988. View at Google Scholar · View at Scopus
  51. T. Kim and T. Adali, “Fully complex multi-layer perceptron network for nonlinear signal processing,” Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, vol. 32, no. 1-2, pp. 29–43, 2002. View at Publisher · View at Google Scholar · View at Scopus
  52. T. Kim and T. Adali, “Fully complex backpropagation for constant envelop signal processing,” in Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, pp. 231–240, Sydney, Australia, December 2000.
  53. T. Kim and T. Adali, “Complex backpropagation neural network using elementary transcendental activation functions,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '01), vol. 2, pp. 1281–1284, Salt Lake City, Utah, USA, May 2001. View at Scopus
  54. G. Georgiou and C. Koutsougeras, “Complex domain backpropagation,” IEEE Transactions on Circuits and Systems II, vol. 39, no. 5, pp. 330–334, 1992. View at Publisher · View at Google Scholar · View at Scopus
  55. C. H. You and D. S. Hong, “The new complex backpropagation algorithm and its applications: adaptive equalization,” Telecommunication Review, vol. 6, no. 2, pp. 133–144, 1996. View at Google Scholar
  56. A. I. Hanna and D. P. Mandic, “A normalised complex backpropagation algorithm,” in Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '02), vol. 1, pp. 977–980, 2002. View at Scopus
  57. H. Leung and S. Haykin, “The complex backpropagation algorithm,” IEEE Transactions on Signal Processing, vol. 39, no. 9, pp. 2101–2104, 1991. View at Publisher · View at Google Scholar · View at Scopus
  58. N. Benvenuto, M. Marchesi, F. Piazza, and A. Uncini, “Non linear satellite radio links equalized using blind neural networks,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '91), vol. 3, pp. 1521–1524, 1991. View at Scopus
  59. N. Benvenuto and F. Piazza, “On the complex backpropagation algorithm,” IEEE Transactions on Signal Processing, vol. 40, no. 4, pp. 967–969, 1992. View at Publisher · View at Google Scholar · View at Scopus
  60. M. Ibnkahla and F. Castanie, “Vector neural networks for digital satellite communications,” in Proceedings of the IEEE International Conference on Communications (ICC '95), vol. 3, pp. 1865–1869, 1995. View at Scopus
  61. A. Uncini, L. Vecci, P. Campolucci, and F. Piazza, “Complex-valued neural networks with adaptive spline activation functions,” IEEE Transactions on Signal Processing, vol. 47, no. 2, pp. 505–514, 1999. View at Google Scholar · View at Scopus
  62. C. You and D. Hong, “Nonlinear blind equalization schemes using complex-valued multilayer feedforward neural networks,” IEEE Transactions on Neural Networks, vol. 9, pp. 1442–1455, 1998. View at Google Scholar
  63. A. Yadav, D. Mishra, S. Ray, R. N. Yadav, and P. K. Kalra, “Representation of complex-valued neural networks: a real-valued approach,” in Proceedings International Conference on Intelligent Sensing and Information Processing (ICISIP '05), pp. 331–335, 2005. View at Publisher · View at Google Scholar · View at Scopus
  64. MRI Basics: MRI Basic, September 2007, http://www.cis.rit.edu/htbooks/mri/inside.htm.
  65. D. G. Nishimura, “Principles of Magnetic Resonance Imaging,” April 1996.