Comparing Autoregressive Moving Average (ARMA) Coefficients Determination using Artificial Neural Networks with Other Techniques
Autoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neural Network technique. In this paper, the method of using Artificial Neural network (ANN) technique is compared with some known and widely acceptable techniques. The comparisons is entirely based on the value of the coefficients obtained. Result obtained shows that the use of ANN also gives accurate in computing the coefficients of an ARMA system.
[1] Z. P. Liang, P. C. Lauterbur, "Principles of Magnetic Resonance Imaging,
A signal processing perspective", IEEE Press, New York, 2000.
[2] A. M. Aibinu, M. J.E. Salami, A. A. Shafie and A. R. Najeeb, " Model
Order Determination for MRI Signal", accepted for publication , 2008.
[3] D. G. Nishimura, "Principles of Magnetic Resonance Imaging", April
1996.
[4] 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. M1-5:3, pp 257 - 261, 1986.
[5] F. J. Harris,"On the Use of Windows for Harmonic Analysis with the
Discrete Fourier Transform", Proceedings of the IEEE. Vol. 66, January
1978.
[6] M. R. Smith, S. T. Nichols, R. Constable and R. Henkelman, "A
quantitative comparison of the TERA modeling and DFT magnetic
resonance image reconstruction techniques", Magn. Reson. Med., Vol.
19 pp. 1-19, 1991.
[7] K. H. Chon, R. J. Cohen, "Linear and Non-Linear ARMA Model Parameter
Estimation Using Artificial Neural Network", IEEE Transactions on
BioMedical Engineering, Vol. 44, No 3, pp 168 - 174, 1997.
[8] 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.
[9] M. H. Hayes, "Staitical Digital Signal processing and Modelling", John
Wiley & Sons, Canada, 1996.
[10] Z. P. Liang, F. E. Boada, R. T. Constable, E. M. Haacke, P. C.
Lauterbur, and M. R. Smith, "Constrained Reconstruction Methods in
MR Imaging", Reviews of MRM, vol. 4, pp.67 - 185, 1992.
[11] E. Hackle and Z. Liang, "Superresolution Reconstruction Through
Object Modeling and Estimation", IEEE transactions in A.S.S.P, 37:
592 - 595, 1989.
[12] E. C. Whitman, "The spectral analysis of discrete time series in terms
of linear regressive models", Naval Ordinance Labs Rep., NOLTR-070-
109, White Oak, MD, June 23, 1974.
[13] R. Palaniappan, "Towards Optimal Model Oreder Selection for Autoregressive
Spectral Analysis of Mental Tasks Using Genetic Algorithm",
IJCSNS International Journal of Computer Science and Network Security,
Vol. 6 No. 1A, January 2006.
[14] H. Akaike, "Power Spectrum Estimation through Autoregression Model
Fitting", Annals of the Institute of Statistical Mathematics, vol. 21, pp.
407-419, 1969.
[15] H. Akaike, "A New Look at the Statistical Model Identification", IEEE
Trans. Autom. Control, vol. AC-19, pp. 716-723, 1974.
[16] J. Rissanen, "Modelling by shortest data description", Automatica,
vol.14, pp.465-471, 1978.
[17] M.J. Salami, A. R. Najeeb, O. Khalifa, K. Arrifin, "MR Reconsturction
with Autoregressive Moving Average", International Conference on
Biotechnology Engineering, Kuala Lumpur, pp 676 - 704, May, 2007.
[1] Z. P. Liang, P. C. Lauterbur, "Principles of Magnetic Resonance Imaging,
A signal processing perspective", IEEE Press, New York, 2000.
[2] A. M. Aibinu, M. J.E. Salami, A. A. Shafie and A. R. Najeeb, " Model
Order Determination for MRI Signal", accepted for publication , 2008.
[3] D. G. Nishimura, "Principles of Magnetic Resonance Imaging", April
1996.
[4] 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. M1-5:3, pp 257 - 261, 1986.
[5] F. J. Harris,"On the Use of Windows for Harmonic Analysis with the
Discrete Fourier Transform", Proceedings of the IEEE. Vol. 66, January
1978.
[6] M. R. Smith, S. T. Nichols, R. Constable and R. Henkelman, "A
quantitative comparison of the TERA modeling and DFT magnetic
resonance image reconstruction techniques", Magn. Reson. Med., Vol.
19 pp. 1-19, 1991.
[7] K. H. Chon, R. J. Cohen, "Linear and Non-Linear ARMA Model Parameter
Estimation Using Artificial Neural Network", IEEE Transactions on
BioMedical Engineering, Vol. 44, No 3, pp 168 - 174, 1997.
[8] 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.
[9] M. H. Hayes, "Staitical Digital Signal processing and Modelling", John
Wiley & Sons, Canada, 1996.
[10] Z. P. Liang, F. E. Boada, R. T. Constable, E. M. Haacke, P. C.
Lauterbur, and M. R. Smith, "Constrained Reconstruction Methods in
MR Imaging", Reviews of MRM, vol. 4, pp.67 - 185, 1992.
[11] E. Hackle and Z. Liang, "Superresolution Reconstruction Through
Object Modeling and Estimation", IEEE transactions in A.S.S.P, 37:
592 - 595, 1989.
[12] E. C. Whitman, "The spectral analysis of discrete time series in terms
of linear regressive models", Naval Ordinance Labs Rep., NOLTR-070-
109, White Oak, MD, June 23, 1974.
[13] R. Palaniappan, "Towards Optimal Model Oreder Selection for Autoregressive
Spectral Analysis of Mental Tasks Using Genetic Algorithm",
IJCSNS International Journal of Computer Science and Network Security,
Vol. 6 No. 1A, January 2006.
[14] H. Akaike, "Power Spectrum Estimation through Autoregression Model
Fitting", Annals of the Institute of Statistical Mathematics, vol. 21, pp.
407-419, 1969.
[15] H. Akaike, "A New Look at the Statistical Model Identification", IEEE
Trans. Autom. Control, vol. AC-19, pp. 716-723, 1974.
[16] J. Rissanen, "Modelling by shortest data description", Automatica,
vol.14, pp.465-471, 1978.
[17] M.J. Salami, A. R. Najeeb, O. Khalifa, K. Arrifin, "MR Reconsturction
with Autoregressive Moving Average", International Conference on
Biotechnology Engineering, Kuala Lumpur, pp 676 - 704, May, 2007.
@article{"International Journal of Information, Control and Computer Sciences:50607", author = "Abiodun M. Aibinu and Momoh J. E. Salami and Amir A. Shafie and Athaur Rahman Najeeb", title = "Comparing Autoregressive Moving Average (ARMA) Coefficients Determination using Artificial Neural Networks with Other Techniques", abstract = "Autoregressive Moving average (ARMA) is a parametric based method of signal representation. It is suitable for problems in which the signal can be modeled by explicit known source functions with a few adjustable parameters. Various methods have been suggested for the coefficients determination among which are Prony, Pade, Autocorrelation, Covariance and most recently, the use of Artificial Neural Network technique. In this paper, the method of using Artificial Neural network (ANN) technique is compared with some known and widely acceptable techniques. The comparisons is entirely based on the value of the coefficients obtained. Result obtained shows that the use of ANN also gives accurate in computing the coefficients of an ARMA system.
", keywords = "Autoregressive moving average, coefficients, back propagation, model parameters, neural network, weight.", volume = "2", number = "6", pages = "1823-6", }
