The Performance of Artificial Bee Colony (ABC) in Structure Selection of Polynomial NARX Models
Keywords:System Identification (SI), Artificial Bee Colony (ABC), Binary Particle Swarm Optimization (BPSO), Nonlinear Auto-Regressive with eXogenous (NARX),
AbstractSystem Identification (SI) is a discipline of building a mathematical model of dynamic systems based on its input and output data. The process of SI is generally divided into structure selection, parameter estimation and model validation. This paper attempts to address the structure selection issue in SI, where the objective is to select the most representative set of regressors to represent the system. However, the selection process must obey the principle of parsimony, where the structure must be as small as possible, yet has the ability to represent the system well. We propose a binarized modification of the Artificial Bee Colony (ABC) algorithm to perform structure selection of a Nonlinear Auto-Regressive with eXogenous (NARX) model on a Direct Current (DC) motor. We compare this implementation with the Binary Particle Swarm Optimization (BPSO) algorithm in terms of solution quality and convergence consistency. The results indicate that the ABC algorithm excelled in terms of convergence consistency with similar solution quality to BPSO algorithm.
Li F. and PengfeiL., “The Research Survey of System Identification Method,” in 5th International Conference on Intelligent HumanMachine Systems and Cybernetics (IHMSC), pp. 397-401, 2013.
LingT. G., RahmatM. F., and HusainA. R., “System identification and control of an Electro-Hydraulic Actuator system,” in IEEE 8th International Colloquium on Signal Processing and its Applications (CSPA), pp. 85-88, 2012.
L. Guangjun, X. Xiaoping, and W. Feng, “Identification of a kind of nonlinear system,” in 2011 Seventh International Conference on Natural Computation (ICNC), pp. 1730-1733, 2011.
KarabogaD. and BasturkB., “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing, vol. 8, pp. 687-697, 2008.
Kennedy J. and EberhartR. C., “A discrete binary version of the particle swarm algorithm,” in Proc. 1997 Conf. Systems, Man, Cybernetics, Piscataway, NJ, pp. 4104–4108, 1997.
TuranogluE., ÖzceylanE., and KiranM. S., “Particle swarm optimization and artificial bee colony approaches to optimize of single input-output fuzzy membership functions,” Proceedings of the 41st International Conference on Computers & Industrial Engineering, pp.
AkayB., “A study on particle swarm optimization and artificial bee colony algorithms for multilevel thresholding,” Applied Soft Computing. 13, pp. 3066-3091, 2013.
AmisigoB. A., GiesenN. v. d., RogersC., AndahW. E. I., and FriesenJ., 2007.Monthly streamflow prediction in the Volta Basin of West Africa: A SISO NARMAX polynomial modelling," Physics and Chemistry of the Earth. 33(1-2):141-150.
RahimN. A., 2004.The Design Of A Non-Linear Autoregressive Moving Average With Exegenous Input (NARMAX) For A DC Motor. MSc, Faculty of Electrical Engineering, UniversitiTeknologi MARA, Shah Alam.
Billings S. A. and WeiH.-L., 2005.A New Class of Wavelet Networks for Nonlinear System Identification. IEEE Trans. Neural Networks. 16(4): 862-874.
DixitG. P., DubeyH. M., PanditM., and PanigrahiB. K., 2011. Artificial bee colony optimization for combined economic load and emission dispatch.in International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2011). 340-345.
I. M. Yassin, M. N. Taib, N. A. Rahim, M. K. M. Salleh, and H. Z. Abidin, 2010. Particle Swarm Optimization for NARX structure selection - Application on DC motor model.in IEEE Symposium on Industrial Electronics & Applications (ISIEA). 456-462.
YassinI. M., TaibM. N., Abdul AzizM. Z., RahimN. A., TahirN. M., and JohariA., 2011. Identification of DC motor drive system model using Radial Basis Function (RBF) Neural Network.in IEEE Industrial
Electronics and Applications (ISIEA). 13-18.
Mendez E. M. A. M. and BillingsS. A., 2001.An alternative solution to the model structure selection problem. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on. 31: 597-608.
EspinozaM., SuykensJ. A. K., and MoorB. D., 2005.Kernel Based Partially Linear Models and Nonlinear Identification. IEEE Trans.Automatic Control. 50(10): 1602-1606.
How to Cite
TRANSFER OF COPYRIGHT AGREEMENT
The manuscript is herewith submitted for publication in the Journal of Telecommunication, Electronic and Computer Engineering (JTEC). It has not been published before, and it is not under consideration for publication in any other journals. It contains no material that is scandalous, obscene, libelous or otherwise contrary to law. When the manuscript is accepted for publication, I, as the author, hereby agree to transfer to JTEC, all rights including those pertaining to electronic forms and transmissions, under existing copyright laws, except for the following, which the author(s) specifically retain(s):
- All proprietary right other than copyright, such as patent rights
- The right to make further copies of all or part of the published article for my use in classroom teaching
- The right to reuse all or part of this manuscript in a compilation of my own works or in a textbook of which I am the author; and
- The right to make copies of the published work for internal distribution within the institution that employs me
I agree that copies made under these circumstances will continue to carry the copyright notice that appears in the original published work. I agree to inform my co-authors, if any, of the above terms. I certify that I have obtained written permission for the use of text, tables, and/or illustrations from any copyrighted source(s), and I agree to supply such written permission(s) to JTEC upon request.