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]  S. Mukherjee,et al.  Relative Mapping Errors of Linear Time Invariant Systems Caused By Particle Swarm Optimized Reduced Order Model , 2007 .

[2]  Dazi Li,et al.  Fractional system identification based on improved NLJ algorithm , 2012, 2012 24th Chinese Control and Decision Conference (CCDC).

[3]  Dervis Karaboga,et al.  A modified Artificial Bee Colony algorithm for real-parameter optimization , 2012, Inf. Sci..

[4]  David E. Goldberg,et al.  AllelesLociand the Traveling Salesman Problem , 1985, ICGA.

[5]  Saban Ozer,et al.  Identification of nonlinear systems using Clonal Selection algorithm , 2009, 2009 IEEE 17th Signal Processing and Communications Applications Conference.

[6]  Dervis Karaboga,et al.  A novel clustering approach: Artificial Bee Colony (ABC) algorithm , 2011, Appl. Soft Comput..

[7]  Y. Shamash Linear system reduction using Pade approximation to allow retention of dominant modes , 1975 .

[8]  Sang-Won Nam,et al.  Application of higher order spectral analysis to cubically nonlinear system identification , 1994, IEEE Trans. Signal Process..

[9]  Rik Pintelon,et al.  System Identification: A Frequency Domain Approach , 2012 .

[10]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[11]  Ibrahim Kaya,et al.  Parameter Estimation for Integrating Processes Using Relay Feedback Control under Static Load Disturbances , 2006 .

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[14]  Dervis Karaboga,et al.  Artificial bee colony algorithm , 2010, Scholarpedia.

[15]  Saban Ozer,et al.  Identification of nonlinear volterra systems using differential evolution algorithm , 2010, National Conference on Electrical, Electronics and Computer Engineering.

[16]  L. Coelho,et al.  Nonlinear model identification of an experimental ball-and-tube system using a genetic programming approach , 2009 .

[17]  Francisco Ballestín,et al.  A double genetic algorithm for the MRCPSP/max , 2011, Comput. Oper. Res..

[18]  Marco P. Schoen,et al.  Intelligent optimization techniques, genetic algorithms, tabu search, simulated annealing, and neural networks, D. T. Pham and D. Karaboga, Springer: Berlin, Heidelberg, New York; Springer London: London, 2000, 302pp, ISBN 1‐85233‐028‐7 , 2005 .

[19]  Yun-Hyung Lee,et al.  System Identification by Real-Coded Genetic Algorithm , 2007 .

[20]  Duc Truong Pham,et al.  Intelligent Optimisation Techniques: Genetic Algorithms, Tabu Search, Simulated Annealing and Neural Networks , 2011 .

[21]  Xiuqin Deng System Identification Based on Particle Swarm Optimization Algorithm , 2009, 2009 International Conference on Computational Intelligence and Security.

[22]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[23]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[24]  Ganesh K. Venayagamoorthy,et al.  Particle swarm optimization with quantum infusion for system identification , 2010, Eng. Appl. Artif. Intell..

[25]  Edward Layer,et al.  Mapping error of linear dynamic systems caused by reduced-order model , 2001, IEEE Trans. Instrum. Meas..

[26]  G. Parmar,et al.  System reduction using eigen spectrum analysis and Padé approximation technique , 2007, Int. J. Comput. Math..

[27]  R. C. Mittal,et al.  Linear time invariant system order reduction using multipoint step response matching , 2007, Int. J. Syst. Sci..

[28]  S. Ozer,et al.  Identification of bilinear systems using differential evolution algorithm , 2011 .

[29]  C. S. Ravichandran,et al.  Designing of PID Controller for Discrete Time Linear System Using Balanced Approach Reduced Order Model , 2007 .