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COMPUTATIONALLY SIMPLE NEURAL NETWORK APPROACH TO DETERMINE PIECEWISE-LINEAR DYNAMICAL MODEL

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F17%3A39910629" target="_blank" >RIV/00216275:25410/17:39910629 - isvavai.cz</a>

  • Alternative codes found

    RIV/00216275:25530/17:39910629

  • Result on the web

    <a href="http://dx.doi.org/10.14311/NNW.2017.27.020" target="_blank" >http://dx.doi.org/10.14311/NNW.2017.27.020</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.14311/NNW.2017.27.020" target="_blank" >10.14311/NNW.2017.27.020</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    COMPUTATIONALLY SIMPLE NEURAL NETWORK APPROACH TO DETERMINE PIECEWISE-LINEAR DYNAMICAL MODEL

  • Original language description

    The article introduces a new technique for nonlinear system modeling. This approach, in comparison to its alternatives, is straight and computationally undemanding. The article employs the fact that once a nonlinear problem is modeled by a piecewise-linear model, it can be solved by many efficient techniques. Thus, the result of introduced technique provides a set of linear equations. Each of the equations is valid in some region of state space and together, they approximate the whole nonlinear problem. The technique is comprehensively described and its advantages are demonstrated on an example.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Neural Network World

  • ISSN

    1210-0552

  • e-ISSN

  • Volume of the periodical

    27

  • Issue of the periodical within the volume

    4

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    21

  • Pages from-to

    351-371

  • UT code for WoS article

    000410411900003

  • EID of the result in the Scopus database