izpis_h1_title_alt

A general approach to statistical modeling of physical laws : nonparametric regression
Grabec, Igor (Author)

URLURL - Presentation file, Visit http://arxiv.org/abs/0704.0089 This link opens in a new window

Abstract
Statistical modeling of experimental physical laws is based on the probability density function of measured variables. It is expressed by experimental data via a kernel estimator. The kernel is determined objectively by the scattering of data during calibration of experimental setup.A physical law, which relates measured variables, is optimally extracted from experimental data by the conditional average estimator. It is derived directly from the kernel estimator and corresponds to a general nonparametric regression. The proposed method is demonstrated by the modeling of a return map of noisy chaotic data. In this example, the nonparametric regression is used to predict a future value of chaotic time series from the present one. The mean predictor error is used in the definition of predictor quality, while the redundancy is expressed by the mean square distance betweendata points. Both statistics are used in a new definition of predictor cost function. From the minimum of the predictor cost function, a proper number of data in the model is estimated.

Language:English
Keywords:fizika, fizikalni zakoni, statistično modeliranje, neparametrične regresije, kernel estimator, experimental information, complexity, redundancy, modeling, physical law, nonparametric regression
Work type:Not categorized (r6)
Organization:FS - Faculty of Mechanical Engineering
Year:2007
Number of pages:17 f.
UDC:53
COBISS.SI-ID:10030875 Link is opened in a new window
Views:720
Downloads:247
Metadata:XML RDF-CHPDL DC-XML DC-RDF
 
Average score:(0 votes)
Your score:Voting is allowed only to logged in users.
:
Share:AddThis
AddThis uses cookies that require your consent. Edit consent...

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Comments

Leave comment

You have to log in to leave a comment.

Comments (0)
0 - 0 / 0
 
There are no comments!

Back