Modeling of higher order systems using artificial bee colony algorithm
DOI:
https://doi.org/10.11121/ijocta.01.2016.00298Keywords:
Artificial bee colony algorithm, system modeling, parameter optimizationAbstract
In this work, modeling of the higher order systems based on the use of the artificial bee colony (ABC) algorithm were examined. Proposed model parameters for the sample systems in the literature were obtained by using the algorithm, and its performance was presented comparatively with the other methods. Simulation results show that the ABC algorithm based system modeling approach can be used as an efficient and powerful method for higher order systems.
Downloads
References
Ljung, L., System Identification: Theory for The User, Prentice Hall, 1987.
Söderström, T., Stoica, P., System Identification, Prentice-Hall, 1989.
Zorlu, H., Özer Ş., Identification of Nonlinear Systems By Using Clonal Selection Algorithm, IEEE 17th Signal Processing and Communications Applications Conference (SIU-2009), Proceedings CD, Antalya, Turkey, 2009 (in Turkish).
Coelho L. S., Pessoa M. W., ''Nonlinear Model İdentification Of An Experimental Ball-And-Tube System Using A Genetic Programming Approach'', Mechanical Systems and Signal Processing, 23(5), 1434–1446, 2009. Crossref
Pintelon R., and Schoukens J., ''System Identification - A Frequency Domain Approach'', IEEE Press, Piscataway, 2001. Crossref
Bağış A., Özçelik Y., System Identification By Using Real Coded Genetic Algorithm, 12th Electrical, Electronics, Computer, Biomedical Engineering National Congress, Eskişehir, Turkey, 2007 (in Turkish).
Zorlu, H., Özer Ş., Identification of Nonlinear Volterra Systems Using Differential Evolution Algorithm, Electrical, Electronics, and Computer Engineering Symposium (ELECO-2010), Proceedings CD, Bursa, Turkey, 2010 (in Turkish).
Luitel B., Venayagamoorthy G.K., ''Particle swarm optimization with quantum infusion for system identification'', Engineering Applications of Artificial Intelligence, vol. 23, pp.635-649, 2010. Crossref
Şenberber H., Bağış A., Performance Investigation of Artificial Bee Colony Algorithm in Modeling of Systems with Delay, Symposium on Innovations in Intelligent Systems and Applications (ASYU'2012), pp.164-168, Trabzon, Turkey, 2012 (in Turkish).
Ozer, S., Zorlu, H., "Identification Of Bilinear Systems Using Differential Evolution Algorithm", Sadhana-Academy Proceedings in Engineering Sciences, Vol. 36, Part 3, pp. 281–292. June 2011. Crossref
Nam S.W., Powers, E.J., "Application Of Higher Order Spectral Analysis To Cubically Nonlinear System Identification", IEEE Trans. on Signal Processing, 42, 1746-1765, 1994. Crossref
Şenberber H., Bağış A., Determination of Model Parameters of Unstable Systems By Using Artificial Bee Colony Algorithm, Automatic Control National Meeting (TOK'2012), pp.812-816, Niğde, Turkey, 2012 (in Turkish).
Deng X., "System Identification Based on Particle Swarm Optimization Algorithm", International Conference on Computational Intelligence and Security (CIS'09), pp. 259-263, 2009. Crossref
Bağış A., Performance Investigation of Partical Swarm Optimization in Higher Order Oscillatory Systems Modeling, Automatic Control National Meeting (TOK'2010), pp.168-173, Kocaeli, Turkey, 2010 (in Turkish).
Ming Z., Dazi L., "Fractional System Identification Based on Improved NLJ Algorithm", Control and Decision Conference (CCDC), 24th Chinese, pp. 1057-1061, 2012.
Yu R., Song Y., Rahardja S., "Lms In Promınent System Subspace For Fast System Identıfıcatıon", 2012 IEEE Statistical Signal Processing Workshop (SSP), pp. 209-212, 2012. Crossref
Şenberber H., Artificial Bee Colony Algorithm Performance Investigation of System Modeling, M.Sc. Thesis, Erciyes University, Graduate School of Natural and Applied Sciences, Kayseri, Turkey, 2012 (in Turkish).
Karaboğa, D., "An Idea Based On Honey Bee Swarm For Numerical Optimization, Tecnical Report TR06", Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
Karaboğa, D., Öztürk, C., 'A Novel Clustering Approach: Artificial Bee Colony (ABC) Algorithm',Applied Soft Computing, Vol 11, 652–657, 2011. Crossref
Karaboğa, D., ''Artificial Bee Colony Algorithm'' Scholarpedia, 5(3):6915, 2010. Crossref
Karaboğa, D., Akay, B., ''A Comparative Study Of Artificial Bee Colony Algorithm'', Applied Mathematics and Computation, Vol 214, 108–132, 2009. Crossref
Akay, B., Karaboğa D, ''A modified Artificial Bee Colony algorithm for real-parameter optimization'', Information Sciences, Vol. 192, pp.120–142, 2012. Crossref
Price, K., Storn, R., "Differential Evolution: Numerical Optimization Made Easy", Dr. Dobb's J. 78, 18–24, 1997.
Storn, R., Price, K., "Differential Evolution: A Simple and Efficient Heuristic For Global Optimization Over Continuous Spaces", Journal of Global Optimization, Vol 11, 341-359, 1997. Crossref
Karaboğa, D., Artificial Intelligence Optimization Algorithms, Nobel Publ., ISBN: 975-6574-37-2, 2004 (in Turkish).
Bağış, A., Savaşçıhabeş, A., Determination of PID Controller Parameters By Using Binary and Real Coded Genetic Algorithm, Symposium on Innovations in Intelligent Systems and Applications (ASYU'2008), pp.198-202, Isparta, Turkey, 2008 (in Turkish).
Pham, D.T., Karaboğa, D., "Intelligent Optimization Techniques: Genetic Algorithms. Tabu Search, Simulated Annealing and Neural Networks", Springer– Verlag, 2000. Crossref
Goldberg, D.E., Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-wesley Publ. Co. Inc., USA, 1989.
Goldberg, D.E., Lingle, R., Alleles, Loci and the Travelling Salesman Problem in Grefenstette J. J., (ed) Proceedings of the 1st International Conference on Genetic Algorithms and their Applications, Carneige-Mellon University, Pittsburgh, Lawrence Erlbaum Assoc, Publishers, 1985.
Barrios A., Ballestin F., Valls V., "A Double Genetic Algorithm Fot The MRCPSP/max", Computer & Operations Research, Vol 38, 33-43, 2011. Crossref
Manigandan T., Devarajan N., Sivanandam S., "Desing of PID Controller Using Reduced Order Model", AOI Journal, Vol. 15, 2005.
Mukherjee S., Satakshi, Mittal, R.C., "Linear time invariant system order reduction using multipoint step response matching", International Journal of Systems Science, Vol. 38, No. 3, 211–217, 2007. Crossref
Shamash Y., "Linear system reduction using Pade approximation to allow retention of dominant modes", International Journal of Control, Vol. 21, No. 2, 257-272, 1975. Crossref
Prasad, R. and Pal, J., "Stable reduction of linear systems by continued fractions", Journal of the Institution of Engineers (India), Part EL, 72, 113–116. 1991
Mittal A.K., Prasad R., and Sharma S.P., "Reduction of linear dynamic systems using an error minimization technique", Journal of Institution of Engineers IE(I) Journal – EL, Vol. 84, pp. 201-206, March 2004.
Parmar G., Mukherjee S., Prasad R., "System reduction using Eigen spectrum analysis and Padé approximation technique", International Journal of Computer Mathematics, Vol.84, No.12, pp. 1871-1880, 2007. Crossref
Parmar G., Mukherjee S., Prasad R., "Relative Mapping Errors of Linear Time Invariant Systems Caused By Particle Swarm Optimized Reduced Order Model", World Academy of Science, Engineering and Technology, Vol. 28, pp. 336-342, 2007.
Layer E., "Mapping Error of Linear Dynamic Systems Caused by Reduced-Order Model", IEEE Transactıons On Instrumentatıon And Measurement, Vol. 50, No. 3, 2001. Crossref
Kaya İ., "Parameter Estimation for Integrating Processes Using Relay Feedback Control under Static Load Disturbances", Ind. Eng. Chem. Res., 45, 4726-4731, 2006. Crossref
Dorf R.C., Bishop R.H., Modern Control Systems-8th Edt., Addison-Wesley Longman, Inc., 1998.
MathWorks, Control System Toolbox for Use with Matlab-Computation, Visualization, Programming, Using the Control System Toolbox-Version 1, The MathWorks, Inc., 2000.
MathWorks, Control System Toolbox Documentation Center, http://www.mathworks.com/help/control/index.html (Accessed: 02/2014)
Downloads
Published
How to Cite
Issue
Section
License
Articles published in IJOCTA are made freely available online immediately upon publication, without subscription barriers to access. All articles published in this journal are licensed under the Creative Commons Attribution 4.0 International License (click here to read the full-text legal code). This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your work will ensure your right to make your work freely and openly available.
Under the Creative Commons Attribution 4.0 International License, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles in IJOCTA, so long as the original authors and source are credited.
The readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
This work is licensed under a Creative Commons Attribution 4.0 International License.
