# Stochastic multivariable self-tuning tracker for non-gaussian systems

International Journal of Applied Mathematics and Computer Science (2005)

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

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topFilipovic, 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 -

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