Stochastic multivariable self-tuning tracker for non-gaussian systems

Vojislav Filipovic

International Journal of Applied Mathematics and Computer Science (2005)

  • Volume: 15, Issue: 3, page 351-357
  • ISSN: 1641-876X

Abstract

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This paper considers the properties of a minimum variance self-tuning tracker for MIMO systems described by ARMAX models. It is assumed that the stochastic noise has a non-Gaussian distribution. Such an assumption introduces into a recursive algorithm a nonlinear transformation of the prediction error. The system under consideration is minimum phase with different dimensions for input and output vectors. In the paper the concept of Kronecker's product is used, which allows us to represent unknown parameters in the form of vectors. For parameter estimation a stochastic approximation algorithm is employed. Using the concept of the stochastic Lyapunov function, global stability and optimality of the feedback system are established.

How to cite

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Filipovic, Vojislav. "Stochastic multivariable self-tuning tracker for non-gaussian systems." International Journal of Applied Mathematics and Computer Science 15.3 (2005): 351-357. <http://eudml.org/doc/207749>.

@article{Filipovic2005,
abstract = {This paper considers the properties of a minimum variance self-tuning tracker for MIMO systems described by ARMAX models. It is assumed that the stochastic noise has a non-Gaussian distribution. Such an assumption introduces into a recursive algorithm a nonlinear transformation of the prediction error. The system under consideration is minimum phase with different dimensions for input and output vectors. In the paper the concept of Kronecker's product is used, which allows us to represent unknown parameters in the form of vectors. For parameter estimation a stochastic approximation algorithm is employed. Using the concept of the stochastic Lyapunov function, global stability and optimality of the feedback system are established.},
author = {Filipovic, Vojislav},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {self-tuning tracker; global stability; optimality; robust statistics; non-Gaussian noise; ARMAX model},
language = {eng},
number = {3},
pages = {351-357},
title = {Stochastic multivariable self-tuning tracker for non-gaussian systems},
url = {http://eudml.org/doc/207749},
volume = {15},
year = {2005},
}

TY - JOUR
AU - Filipovic, Vojislav
TI - Stochastic multivariable self-tuning tracker for non-gaussian systems
JO - International Journal of Applied Mathematics and Computer Science
PY - 2005
VL - 15
IS - 3
SP - 351
EP - 357
AB - This paper considers the properties of a minimum variance self-tuning tracker for MIMO systems described by ARMAX models. It is assumed that the stochastic noise has a non-Gaussian distribution. Such an assumption introduces into a recursive algorithm a nonlinear transformation of the prediction error. The system under consideration is minimum phase with different dimensions for input and output vectors. In the paper the concept of Kronecker's product is used, which allows us to represent unknown parameters in the form of vectors. For parameter estimation a stochastic approximation algorithm is employed. Using the concept of the stochastic Lyapunov function, global stability and optimality of the feedback system are established.
LA - eng
KW - self-tuning tracker; global stability; optimality; robust statistics; non-Gaussian noise; ARMAX model
UR - http://eudml.org/doc/207749
ER -

References

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