Modeling of higher order systems using artificial bee colony algorithm

Modeling of higher order systems using artificial bee colony algorithm

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.

___

  • [1] Ljung, L., System Identification: Theory for The User, Prentice Hall, 1987.
  • [2] Söderström, T., Stoica, P., System Identification, Prentice-Hall, 1989.
  • [3] 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).
  • [4] 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.
  • [5] Pintelon R., and Schoukens J., ''System Identification - A Frequency Domain Approach'', IEEE Press, Piscataway, 2001.
  • [6] 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).
  • [7] 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).
  • [8] 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.
  • [9] 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).
  • [10] 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.
  • [12] 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).
  • [13] Deng X., "System Identification Based on Particle Swarm Optimization Algorithm", International Conference on Computational Intelligence and Security (CIS'09), pp. 259- 263, 2009.
  • [14] 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).
  • [15] Ming Z., Dazi L., "Fractional System Identification Based on Improved NLJ Algorithm", Control and Decision Conference (CCDC), 24th Chinese, pp. 1057-1061, 2012.
  • [16] 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.
  • [17] 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).
  • [18] Karaboğa, D., "An Idea Based On Honey Bee Swarm For Numerical Optimization, Tecnical Report TR06", Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
  • [19] Karaboğa, D., Öztürk, C., 'A Novel Clustering Approach: Artificial Bee Colony (ABC) Algorithm',Applied Soft Computing, Vol 11, 652-657, 2011.
  • [20] Karaboğa, D., ''Artificial Bee Colony Algorithm'' Scholarpedia, 5(3):6915, 2010.
  • [21] Karaboğa, D., Akay, B., ''A Comparative Study Of Artificial Bee Colony Algorithm'', Applied Mathematics and Computation, Vol 214, 108-132, 2009.
  • [22] Akay, B., Karaboğa D,. ''A modified Artificial Bee Colony algorithm for real-parameter optimization'', Information Sciences, Vol. 192, pp.120-142, 2012.
  • [23] Price, K., Storn, R., "Differential Evolution: Numerical Optimization Made Easy", Dr. Dobb's J. 78, 18-24, 1997.
  • [24] 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.
  • [25] Karaboğa, D., Artificial Intelligence Optimization Algorithms, Nobel Publ., ISBN: 975-6574-37-2, 2004 (in Turkish).
  • [26] 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).
  • [27] Pham, D.T., Karaboğa, D., "Intelligent Optimization Techniques: Genetic Algorithms. Tabu Search, Simulated Annealing and Neural Networks", Springer- Verlag, 2000.
  • [28] Goldberg, D.E., Genetic Algorithms in Search, Optimisation and Machine Learning, Addisonwesley Publ. Co. Inc., USA, 1989.
  • [29] 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, CarneigeMellon University, Pittsburgh, Lawrence Erlbaum Assoc, Publishers, 1985.
  • [30] Barrios A., Ballestin F., Valls V., "A Double Genetic Algorithm Fot The MRCPSP/max", Computer & Operations Research, Vol 38, 33- 43, 2011.
  • [31] Manigandan T. , Devarajan N. , Sivanandam S., "Desing of PID Controller Using Reduced Order Model", AOI Journal, Vol. 15, 2005.
  • [32] 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.
  • [33] 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
  • [34] 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
  • [35] 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.
  • [36] 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.
  • [37] 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.
  • [38] Layer E., "Mapping Error of Linear Dynamic Systems Caused by Reduced-Order Model", IEEE Transactıons On Instrumentatıon And Measurement, Vol. 50, No. 3, 2001.
  • [39] Kaya İ., "Parameter Estimation for Integrating Processes Using Relay Feedback Control under Static Load Disturbances", Ind. Eng. Chem. Res., 45, 4726-4731, 2006.
  • [40] Dorf R.C., Bishop R.H., Modern Control Systems-8th Edt., Addison-Wesley Longman, Inc., 1998.
  • [41] MathWorks, Control System Toolbox for Use with Matlab-Computation, Visualization, Programming, Using the Control System Toolbox-Version 1, The MathWorks, Inc., 2000.
  • [42] MathWorks, Control System Toolbox Documentation Center, http://www.mathworks.com/help/control/index .html (Accessed: 02/2014)