Control Law and Pseudo Neural Networks Synthesized by Evolutionary Symbolic Regression Technique

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F16%3A43875601" target="_blank" >RIV/70883521:28140/16:43875601 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-319-33786-9_9" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-319-33786-9_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-33786-9_9" target="_blank" >10.1007/978-3-319-33786-9_9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Control Law and Pseudo Neural Networks Synthesized by Evolutionary Symbolic Regression Technique

Popis výsledku v původním jazyce

This research deals with synthesis of final complex expressions by means of an evolutionary symbolic regression technique-analytic programming (AP)-for novel approach to classification and system control. In the first case, classification technique-pseudo neural network is synthesized, i.e. relation between inputs and outputs created. The inspiration came from classical artificial neural networks where such a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights. AP will synthesize a whole expression at once. The latter case, the AP will create chaotic controller that secures the stabilization of stable state and high periodic orbit-oscillations between several values of discrete chaotic system. Both cases will produce a mathematical relation with several inputs, the latter case uses several historical values from the time series. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used.

Název v anglickém jazyce

Control Law and Pseudo Neural Networks Synthesized by Evolutionary Symbolic Regression Technique

Popis výsledku anglicky

This research deals with synthesis of final complex expressions by means of an evolutionary symbolic regression technique-analytic programming (AP)-for novel approach to classification and system control. In the first case, classification technique-pseudo neural network is synthesized, i.e. relation between inputs and outputs created. The inspiration came from classical artificial neural networks where such a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights. AP will synthesize a whole expression at once. The latter case, the AP will create chaotic controller that secures the stabilization of stable state and high periodic orbit-oscillations between several values of discrete chaotic system. Both cases will produce a mathematical relation with several inputs, the latter case uses several historical values from the time series. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used.

Druh

C - Kapitola v odborné knize

CEP obor

IN - Informatika

OECD FORD obor

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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

2016

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Seminal Contributions to Modelling and Simulation: 30 Years of the European Council of Modelling and Simulation

ISBN

978-3-319-33785-2

Počet stran výsledku

23

Strana od-do

91-113

Počet stran knihy

292

Název nakladatele

Springer International Publishing AG

Místo vydání

Basel

Kód UT WoS kapitoly

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