Different Approaches for constant estimation in analytic programming

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F17%3A63517335" target="_blank" >RIV/70883521:28140/17:63517335 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/70883521:28120/17:63517335

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Different Approaches for constant estimation in analytic programming

Popis výsledku v původním jazyce

This research deals with different approaches for constant estimation in analytic programming (AP). AP is a tool for symbolic regression tasks which enables to synthesise an analytical solution based on the required behaviour of the system. Some tasks do not need any constant estimation - AP is used in its basic version without any constant estimation handling. Compared to this, cases like data approximation need constants (coefficients) which are essential for the process of precise solution synthesis. This paper offers another strategy to already known and used by the AP from the very beginning and approaches published recently in 2016. This paper compares these procedures and the discussion also includes nonlinear fitting and metaevolutionary approach. As the main evolutionary algorithm, a differential algorithm (de/rand/1/bin) for the main process of AP is used.

Název v anglickém jazyce

Different Approaches for constant estimation in analytic programming

Popis výsledku anglicky

This research deals with different approaches for constant estimation in analytic programming (AP). AP is a tool for symbolic regression tasks which enables to synthesise an analytical solution based on the required behaviour of the system. Some tasks do not need any constant estimation - AP is used in its basic version without any constant estimation handling. Compared to this, cases like data approximation need constants (coefficients) which are essential for the process of precise solution synthesis. This paper offers another strategy to already known and used by the AP from the very beginning and approaches published recently in 2016. This paper compares these procedures and the discussion also includes nonlinear fitting and metaevolutionary approach. As the main evolutionary algorithm, a differential algorithm (de/rand/1/bin) for the main process of AP is used.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings - 31st European Conference on Modelling and Simulation, ECMS 2017

ISBN

978-099324404-9

ISSN

—

e-ISSN

neuvedeno

Počet stran výsledku

7

Strana od-do

326-332

Název nakladatele

European Council for Modelling and Simulation

Místo vydání

Madrid

Místo konání akce

Budapešť

Datum konání akce

23. 5. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—