Simulation results show that the ABC algorithm based system modeling approach can be used as an efficient and powerful method for higher order systems.
📌 3 Key Takeaways
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Objective & Hypothesis:In this work, modeling of the higher order systems based on the use of the artificial bee colony (ABC) algorithm were examined.
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Key Findings & Data: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.
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Conclusion & Impact:Simulation results show that the ABC algorithm based system modeling approach can be used as an efficient and powerful method for higher order systems.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters
Abstract
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.
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Cite This Article
BAGİS, A., SENBERBER, H. (2022). Modeling of higher order systems using artificial bee colony algorithm. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), Cilt: 6 - Sayı: 2, 129–139. https://ajindex.com/articles/440693
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
Pintelon R., and Schoukens J., ''System Identification - A Frequency Domain Approach'', IEEE Press, Piscataway, 2001
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
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, [11] 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
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
Bağış A., Performance Investigation of Partical Swarm Optimization in Higher Order Oscillatory Systems Mod