ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)


For citation:

Bezruchko B. P., Smirnov D. A., Sysoev I. V. Reconstruction with hidden variables: modified Bock’s approach. Izvestiya VUZ. Applied Nonlinear Dynamics, 2004, vol. 12, iss. 6, pp. 93-104. DOI: 10.18500/0869-6632-2004-12-6-93-104

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text PDF(Ru):
(downloads: 0)
Language: 
Russian
Article type: 
Article
UDC: 
530.18

Reconstruction with hidden variables: modified Bock’s approach

Autors: 
Bezruchko Boris Petrovich, Saratov State University
Smirnov Dmitrij Alekseevich, Saratov Branch of Kotel`nikov Institute of Radiophysics and Electronics of Russian Academy of Sciences
Sysoev Ilya Vyacheslavovich, Saratov State University
Abstract: 

The task of model parameter estimation from experimental time series under the assumption that some variables cannot be measured or their series are very noisy is considered. Based on proposed quantitative criteria two algorithms — original Bock’s approach and its modification that allows discontinuity of model trajectory are compared. The modified method is shown to be significantly more efficient for long series. Also with the modified approach the worse starting guesses for parameters to be estimated are acceptable.

Key words: 
Acknowledgments: 
The work was dupported by the RFBR (05-02-16305), CRDF (REC-006) and Russian Presidential grant for young scientists (MK-1067.2004.2).
Reference: 
  1. Ljung. Systems identification. Theory for users.  Moskow: Nauka, 1991. P. 432.
  2. Makarenko NG. Embedology and neuroprognosis. Lectures on neuroinformatics. In: V All-Russian Scientific and Technical Conference «Neuroinformatics-2003». Moskow: MIPHI; 2003. P. 86–148.
  3. Horbelt W, Timmer J, Bunner MJ, Meucci В, Ciofini M. Identifying physical properties of а CO2 laser by dynamical modeling оf measured time series. Phys. Rev. Е. 2001;64(1):016222. DOI: 10.1103/PhysRevE.64.016222.
  4. Hegger R, Kantz H, Schmuser Е, Diestelhorst M, Kapsch RP, Beige H. Dynamical properties of a ferroelectric capacitors observed through nonlinear time series analysis. Woodbury: Chaos. 1998;8(3):727–754. DOI: 10.1063/1.166356.
  5. Timmer J, Rust H, Horbelt W, Voss H. Parametric, nonparametric and parametric modelling of а chaotic circuit time series. Phys. Lett. А. 2000;274:123–134. DOI: 10.1016/S0375-9601%2800%2900548-X.
  6. Tokuda I, Parlitz U, llling L, Kennel М, Abarbanel HDI. Parameter estimation for neurons. In: Experimental Chaos, Proceedings of the 7th Experimental Chaos Conference. 26-29 August 2002, San Diego, USA: American Institute of Physics; 2003. P. 410.
  7. Swameye I, Muller TG, Timmer J, Sandra O, Klingmuller U. Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling. USA: Proc. Natl. Acad. Sci. 2003;100:1028-1033. DOI: 10.1073/pnas.0237333100.
  8. Bock H. Modelling оf Chemical Reaction Systems. in: Ebert K, Deuflhard P, Jager W, editors. Proceedings of an International Workshop, Heidelberg, Fed. Rep. of Germany. September 1-5, 1980 Berlin: Springer; 1981;18(8):102–125.
  9. Baake E, Baake M, Bock HG, Briggs KM. Fitting ordinary differential equations to chaotic data. Phys. Rev. А. 1992;45(8):5524-5529. DOI: 10.1103/PhysRevA.45.5524.
  10. Pisarenko КЕ, Sornette D. Statistical methods оf parameter estimation for deterministically chaotic time series. Phys. Rev. Е. 2004;69(3):036122. DOI: 10.1103/PhysRevE.69.036122.
  11. Horbelt W. Maximum likelihood estimation in dynamical systems. PhD Thesis. Freiburg: University оf Freiburg; 2001.
Received: 
22.11.2004
Accepted: 
15.05.2005
Published: 
15.06.2005