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Application of optimization heuristics for complex astronomical object model identification

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F16%3A43897731" target="_blank" >RIV/60461373:22340/16:43897731 - isvavai.cz</a>

  • Result on the web

    <a href="http://link.springer.com/content/pdf/10.1007%2Fs00500-014-1527-y.pdf" target="_blank" >http://link.springer.com/content/pdf/10.1007%2Fs00500-014-1527-y.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/s00500-014-1527-y" target="_blank" >10.1007/s00500-014-1527-y</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Application of optimization heuristics for complex astronomical object model identification

  • Original language description

    Detection and localization of astronomical objects are two of the most fundamental topics in astronomical science where localization uses detection results. Object localization is based on modeling of point spread function and estimation of its parameters. Commonly used models as Gauss or Moffat in objects localization provide good approximation of analyzed objects but cannot be sufficient in the case of exact applications such as object energy estimation. Thus the use of sophisticated models is upon the place. One of the key roles plays also the way of the objective function estimation. The least square method is often used, but it expects data with normal distribution, thus there is a question of a maximum likelihood method application. Another important factor of presented problem is choice of the right optimization method. Classical methods for objective function minimization usually require a good initial estimate for all parameters and differentiation of the objective function with respect to model parameters. The results indicated that stochastic methods such as simulated annealing or harmony search achieved better results than the classical optimization methods.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2016

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Soft Computing

  • ISSN

    1433-7479

  • e-ISSN

  • Volume of the periodical

    20

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    DE - GERMANY

  • Number of pages

    16

  • Pages from-to

    621-636

  • UT code for WoS article

    000372297200014

  • EID of the result in the Scopus database

    2-s2.0-84955660382