Abstract
For system identification, the ordinary differential equations (ODEs) model is popular for its accuracy and effectiveness. Consequently, the ODEs model is extended to the stochastic differential equations (SDEs) model to tackle the stochastic case intuitively. But the existence of stochastic integral is a rigid barrier. We simply transform the SDEs to their corresponding stochastic difference equations (SDCEs) to eliminate stochastic integrals and propose an easy but effective solution to stochastic system identification. In this solution, the maximum likelihood estimation can be applied and the evolutionary algorithms are used to determine structures and parameters of the unknown system.
This is a preview of subscription content, log in via an institution to check access.
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
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science, pp. 87–129 (1995)
Iba, H.: Inference of differential equation models by genetic programming. Information Sciences 178(23), 4453–4468 (2008)
Oltean, M., Groşan, C.: Evolving evolutionary algorithms using multi expression programming. In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 651–658. Springer, Heidelberg (2003)
Shoji, I., Ozaki, T.: Estimation for nonlinear stochastic differential equations by a local linearization method. Stochastic Analysis and Applications 16(4), 733–752 (1998)
Tian, T.: Stochastic models for inferring genetic regulation from microarray gene eexpression data. BioSystems 99(3), 192–200 (2010)
Vasicek, O.: An equilibrium characterization of the term structure. Journal of Financial Economics 5(1), 177–188 (1977)
Wang, P.: Three-stage stochastic runge ckutta methods for stochastic differential equations. Journal of Computational and Applied Mathematics 222(2), 324–332 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cao, Y., Chen, Y., Zhao, Y. (2012). Stochastic System Identification by Evolutionary Algorithms. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-24553-4_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24552-7
Online ISBN: 978-3-642-24553-4
eBook Packages: Computer ScienceComputer Science (R0)