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    Rights statement: Copyright 2005 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.610457

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Reconstruction of stochastic nonlinear models from trajectory measurements

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Reconstruction of stochastic nonlinear models from trajectory measurements. / Luchinsky, Dmitry G.; Smelyanskiy, V. N.; Millonas, M. et al.
In: Proceedings of SPIE, Vol. 5845, 2005, p. 173-181.

Research output: Contribution to Journal/Magazine › Journal article

Harvard

Luchinsky, DG, Smelyanskiy, VN, Millonas, M & McClintock, PVE 2005, 'Reconstruction of stochastic nonlinear models from trajectory measurements', Proceedings of SPIE, vol. 5845, pp. 173-181. https://doi.org/10.1117/12.610457

APA

Luchinsky, D. G., Smelyanskiy, V. N., Millonas, M., & McClintock, P. V. E. (2005). Reconstruction of stochastic nonlinear models from trajectory measurements. Proceedings of SPIE, 5845, 173-181. https://doi.org/10.1117/12.610457

Vancouver

Luchinsky DG, Smelyanskiy VN, Millonas M, McClintock PVE. Reconstruction of stochastic nonlinear models from trajectory measurements. Proceedings of SPIE. 2005;5845:173-181. doi: 10.1117/12.610457

Author

Luchinsky, Dmitry G. ; Smelyanskiy, V. N. ; Millonas, M. et al. / Reconstruction of stochastic nonlinear models from trajectory measurements. In: Proceedings of SPIE. 2005 ; Vol. 5845. pp. 173-181.

Bibtex

@article{6cb9851a84d840c6b967692eadf41ad6,
title = "Reconstruction of stochastic nonlinear models from trajectory measurements",
abstract = "We consider the following general problem of applied stochastic nonlinear dynamics. We observe a time series of signals y(t) = y(t0+hn) corrupted by noise. The actual state and the nonlinear vector field of the dynamical system is not known. The question is how and with what accuracy can we determine x(t) and functional form of f(x). In this talk we discuss a novel approach to the solution of this problem based on the application of the path-integral approach to the full Bayesian inference. We demonstrate a reconstruction of a dynamical state of a system from corrupted by noise measurements. Next we reconstruct the corresponding nonlinear vector field. The emphasis are on the theoretical analysis. The results are compared with the results of earlier research.",
author = "Luchinsky, {Dmitry G.} and Smelyanskiy, {V. N.} and M. Millonas and McClintock, {Peter V. E.}",
note = "Copyright 2005 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.610457 ",
year = "2005",
doi = "10.1117/12.610457",
language = "English",
volume = "5845",
pages = "173--181",
journal = "Proceedings of SPIE",
issn = "0277-786X",
publisher = "SPIE",

}

RIS

TY - JOUR

T1 - Reconstruction of stochastic nonlinear models from trajectory measurements

AU - Luchinsky, Dmitry G.

AU - Smelyanskiy, V. N.

AU - Millonas, M.

AU - McClintock, Peter V. E.

N1 - Copyright 2005 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. http://dx.doi.org/10.1117/12.610457

PY - 2005

Y1 - 2005

N2 - We consider the following general problem of applied stochastic nonlinear dynamics. We observe a time series of signals y(t) = y(t0+hn) corrupted by noise. The actual state and the nonlinear vector field of the dynamical system is not known. The question is how and with what accuracy can we determine x(t) and functional form of f(x). In this talk we discuss a novel approach to the solution of this problem based on the application of the path-integral approach to the full Bayesian inference. We demonstrate a reconstruction of a dynamical state of a system from corrupted by noise measurements. Next we reconstruct the corresponding nonlinear vector field. The emphasis are on the theoretical analysis. The results are compared with the results of earlier research.

AB - We consider the following general problem of applied stochastic nonlinear dynamics. We observe a time series of signals y(t) = y(t0+hn) corrupted by noise. The actual state and the nonlinear vector field of the dynamical system is not known. The question is how and with what accuracy can we determine x(t) and functional form of f(x). In this talk we discuss a novel approach to the solution of this problem based on the application of the path-integral approach to the full Bayesian inference. We demonstrate a reconstruction of a dynamical state of a system from corrupted by noise measurements. Next we reconstruct the corresponding nonlinear vector field. The emphasis are on the theoretical analysis. The results are compared with the results of earlier research.

U2 - 10.1117/12.610457

DO - 10.1117/12.610457

M3 - Journal article

VL - 5845

SP - 173

EP - 181

JO - Proceedings of SPIE

JF - Proceedings of SPIE

SN - 0277-786X

ER -