Abstract
The paper presents a method for the identification of bilinear system parameters by using an improved Genetic Algorithm. Good results could still be obtained when the system output was influenced by Gaussian noise in the simulation. By comparing with RLS and COR through a simulation experiment to a SISO bilinear system, it is found that the method can get better result than the other two methods. Through a simulation experiment to a MIMO bilinear system, the method can get reasonably good results too. These simulations show that the method is simpler and can get better results than RLS and COR. Through a simulation study to an MIMO bilinear system, good results can still be got. In the last section, the paper describes that a hybrid GA, the combination of Genetic Algorithm and nonlinear Least Square, was developed to identify bilinear system structure and parameters simultaneously.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hua, X.: Modeling and Control of Bilinear System. Northeast Chemical Engineering Institute Press (1990)
Goldkberg, D.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)
Kristinsson, K.: System Identification and Control Using Genetic Algorithms. IEEE Trans. Syst., Man, Cyber. 12(5), 1033–1046 (1992)
Zhang, G., Liu, G.: Selecting crossover site with unequal probability in genetic algorithms. Information and Control 26, 53–60 (1997)
Srinivas, M., Patnaik, L.: Adaptive probabilities of crossover and mutation in genetic algorithm. IEEE Trans. Syst., Man and Cyber. SMC-24(4), 656–666 (1994)
Fang, C., Xiao, D.: Process Identification. TsinghuaPress, Beijing (1988)
Wang, Z.: Nonlinear System Identification Based on Genetic Algorithm. Dalian University of technology, M.Sc. thesis (1996)
Wang, Z., Gu, H.: Grey Identification of Cobb-Douglas Production Function Model Parameters. System Engineering and Electronic Technology 7, 19–21 (1999)
Castillo, O., Melin, P., Montiel, O., Sepúlveda, R.: Application of A Breeder Genetic Algorithm For System Identification In An Adaptive Finite Impulse Response Filter. Evolvable Hardware, 146–156 (2001)
Konak, A., Smith, A.: A hybrid genetic algorithm approach for backbone design of communication networks. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 112–117. IEEE, Washington DC (1999)
Castillo, O., Melin, P., Montiel, O., Sepúlveda, R.: Evolutionary Non-Linear System Identification. IC-AI, 98–104 (2004)
Akramizadeh, A., Akbar Farjami, A., Khaloozadeh, H.: Nonlinear Hammerstein Model Identification Using Genetic Algorithm. In: ICAIS 2002, pp. 755–764 (2002)
Castillo, O., Melin, P., Montiel, O., Sepúlveda, R.: The evolutionary learning rule for system identification. Appl. Soft Comput. 3, 343–352 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, Z., Gu, H. (2007). Parameter Identification of Bilinear System Based on Genetic Algorithm. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds) Bio-Inspired Computational Intelligence and Applications. LSMS 2007. Lecture Notes in Computer Science, vol 4688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74769-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-74769-7_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74768-0
Online ISBN: 978-3-540-74769-7
eBook Packages: Computer ScienceComputer Science (R0)