Evolutionary synchronization of chaotic systems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F10%3A00001067" target="_blank" >RIV/70883521:28140/10:00001067 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-642-10707-8_12" target="_blank" >http://dx.doi.org/10.1007/978-3-642-10707-8_12</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-10707-8_12" target="_blank" >10.1007/978-3-642-10707-8_12</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evolutionary synchronization of chaotic systems

Popis výsledku v původním jazyce

This chapter introduces a simple investigation on deterministic chaos synchronization by means of selected evolutionary techniques. Five evolutionary algorithms has been used for chaos synchronization here: differential evolution, self-organizing migrating algorithm, genetic algorithm, simulated annealing and evolutionary strategies in a total of 15 versions. Experiments in this chapter has been done with two different coupled systems (master ? slave) ? Rössler-Lorenz and Lorenz-Lorenz. The main aim ofthis chapter was to show that evolutionary algorithms, under certain conditions, are capable of synchronization of, at least, simple chaotic systems, when the cost function is properly defined as well as the parameters of selected evolutionary algorithm.This chapter consists of two different case studies. For all algorithms each simulation was 100 times repeated to show and check the robustness of proposed methods and experiment configurations.

Název v anglickém jazyce

Evolutionary synchronization of chaotic systems

Popis výsledku anglicky

This chapter introduces a simple investigation on deterministic chaos synchronization by means of selected evolutionary techniques. Five evolutionary algorithms has been used for chaos synchronization here: differential evolution, self-organizing migrating algorithm, genetic algorithm, simulated annealing and evolutionary strategies in a total of 15 versions. Experiments in this chapter has been done with two different coupled systems (master ? slave) ? Rössler-Lorenz and Lorenz-Lorenz. The main aim ofthis chapter was to show that evolutionary algorithms, under certain conditions, are capable of synchronization of, at least, simple chaotic systems, when the cost function is properly defined as well as the parameters of selected evolutionary algorithm.This chapter consists of two different case studies. For all algorithms each simulation was 100 times repeated to show and check the robustness of proposed methods and experiment configurations.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BD - Teorie informace

OECD FORD obor

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Projekt

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Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název periodika

Studies in Computational Intelligence

ISSN

1860-949X

e-ISSN

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Svazek periodika

267

Číslo periodika v rámci svazku

Neuveden

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

22

Strana od-do

385-407

Kód UT WoS článku

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EID výsledku v databázi Scopus

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