The use of moving least squares for a smooth approximation of sampled data

1459 Ogledov
1084 Prenosov
Izvoz citacije: ABNT
GREŠOVNIK, Igor .
The use of moving least squares for a smooth approximation of sampled data. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 53, n.9, p. 582-598, august 2017. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/the-use-of-moving-least-squares-for-a-smooth-approximation-of-sampled-data/>. Date accessed: 06 dec. 2022. 
doi:http://dx.doi.org/.
Grešovnik, I.
(2007).
The use of moving least squares for a smooth approximation of sampled data.
Strojniški vestnik - Journal of Mechanical Engineering, 53(9), 582-598.
doi:http://dx.doi.org/
@article{.,
	author = {Igor  Grešovnik},
	title = {The use of moving least squares for a smooth approximation of sampled data},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {53},
	number = {9},
	year = {2007},
	keywords = {sample data; function approximation; moving least squares methods; smoothing; optimization; },
	abstract = {The use of the moving least-squares approximation for the smoothing of data and the approximation of noisy response functions is presented. The approximation properties are treated with respect to the level of noise, the sampling density and the effective range of the sample. The applicability of the method is demonstrated by smoothing experimental measurement data and solving an optimization problem with a noisy response by the successive response approximation technique. The results indicate that the moving least-squares approximation is applicable to a wide variety of problems due to its smoothness, its accurate approximation over arbitrarily large domains using low-order basis functions, its ability to deal with an irregular arrangement of sampling points and to adapt to different modes of the approximation function in different regions.   },
	issn = {0039-2480},	pages = {582-598},	doi = {},
	url = {https://www.sv-jme.eu/sl/article/the-use-of-moving-least-squares-for-a-smooth-approximation-of-sampled-data/}
}
Grešovnik, I.
2007 August 53. The use of moving least squares for a smooth approximation of sampled data. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 53:9
%A Grešovnik, Igor 
%D 2007
%T The use of moving least squares for a smooth approximation of sampled data
%B 2007
%9 sample data; function approximation; moving least squares methods; smoothing; optimization; 
%! The use of moving least squares for a smooth approximation of sampled data
%K sample data; function approximation; moving least squares methods; smoothing; optimization; 
%X The use of the moving least-squares approximation for the smoothing of data and the approximation of noisy response functions is presented. The approximation properties are treated with respect to the level of noise, the sampling density and the effective range of the sample. The applicability of the method is demonstrated by smoothing experimental measurement data and solving an optimization problem with a noisy response by the successive response approximation technique. The results indicate that the moving least-squares approximation is applicable to a wide variety of problems due to its smoothness, its accurate approximation over arbitrarily large domains using low-order basis functions, its ability to deal with an irregular arrangement of sampling points and to adapt to different modes of the approximation function in different regions.   
%U https://www.sv-jme.eu/sl/article/the-use-of-moving-least-squares-for-a-smooth-approximation-of-sampled-data/
%0 Journal Article
%R 
%& 582
%P 17
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 53
%N 9
%@ 0039-2480
%8 2017-08-18
%7 2017-08-18
Grešovnik, Igor.
"The use of moving least squares for a smooth approximation of sampled data." Strojniški vestnik - Journal of Mechanical Engineering [Online], 53.9 (2007): 582-598. Web.  06 Dec. 2022
TY  - JOUR
AU  - Grešovnik, Igor 
PY  - 2007
TI  - The use of moving least squares for a smooth approximation of sampled data
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - sample data; function approximation; moving least squares methods; smoothing; optimization; 
N2  - The use of the moving least-squares approximation for the smoothing of data and the approximation of noisy response functions is presented. The approximation properties are treated with respect to the level of noise, the sampling density and the effective range of the sample. The applicability of the method is demonstrated by smoothing experimental measurement data and solving an optimization problem with a noisy response by the successive response approximation technique. The results indicate that the moving least-squares approximation is applicable to a wide variety of problems due to its smoothness, its accurate approximation over arbitrarily large domains using low-order basis functions, its ability to deal with an irregular arrangement of sampling points and to adapt to different modes of the approximation function in different regions.   
UR  - https://www.sv-jme.eu/sl/article/the-use-of-moving-least-squares-for-a-smooth-approximation-of-sampled-data/
@article{{}{.},
	author = {Grešovnik, I.},
	title = {The use of moving least squares for a smooth approximation of sampled data},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {53},
	number = {9},
	year = {2007},
	doi = {},
	url = {https://www.sv-jme.eu/sl/article/the-use-of-moving-least-squares-for-a-smooth-approximation-of-sampled-data/}
}
TY  - JOUR
AU  - Grešovnik, Igor 
PY  - 2017/08/18
TI  - The use of moving least squares for a smooth approximation of sampled data
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 53, No 9 (2007): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - sample data, function approximation, moving least squares methods, smoothing, optimization, 
N2  - The use of the moving least-squares approximation for the smoothing of data and the approximation of noisy response functions is presented. The approximation properties are treated with respect to the level of noise, the sampling density and the effective range of the sample. The applicability of the method is demonstrated by smoothing experimental measurement data and solving an optimization problem with a noisy response by the successive response approximation technique. The results indicate that the moving least-squares approximation is applicable to a wide variety of problems due to its smoothness, its accurate approximation over arbitrarily large domains using low-order basis functions, its ability to deal with an irregular arrangement of sampling points and to adapt to different modes of the approximation function in different regions.   
UR  - https://www.sv-jme.eu/sl/article/the-use-of-moving-least-squares-for-a-smooth-approximation-of-sampled-data/
Grešovnik, Igor"The use of moving least squares for a smooth approximation of sampled data" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 53 Number 9 (18 August 2017)

Avtorji

Inštitucije

  • Centre for Computational Continuum Mechanics - C3M, Ljubljana, Slovenia

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 53(2007)9, 582-598
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.

The use of the moving least-squares approximation for the smoothing of data and the approximation of noisy response functions is presented. The approximation properties are treated with respect to the level of noise, the sampling density and the effective range of the sample. The applicability of the method is demonstrated by smoothing experimental measurement data and solving an optimization problem with a noisy response by the successive response approximation technique. The results indicate that the moving least-squares approximation is applicable to a wide variety of problems due to its smoothness, its accurate approximation over arbitrarily large domains using low-order basis functions, its ability to deal with an irregular arrangement of sampling points and to adapt to different modes of the approximation function in different regions.   

sample data; function approximation; moving least squares methods; smoothing; optimization;