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Evolutionary Control of CML Systems

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dc.title Evolutionary Control of CML Systems en
dc.contributor.author Zelinka, Ivan
dc.relation.ispartof Evolutionary Algorithms and Chaotic Systems
dc.identifier.isbn 978-3-642-10706-1
dc.date.issued 2010
dc.event.location Heidelberg
utb.event.state-en Germany
utb.event.state-cs Německo
dc.type bookPart
dc.language.iso en
dc.publisher Springer-Verlag. Berlin
dc.subject Evolutionary algorithms en
dc.subject chaos en
dc.subject control en
dc.subject information en
dc.description.abstract This chapter is a continuation of an investigation on deterministic spa- tiotemporal chaos real-time control by means of selected evolutionary techniques. Real-time like behavior is specially defined and simulated with spatiotemporal chaos model based on mutually nonlineary joined n equations, so called Coupled Map Lattices. In total five evolutionary algorithms has been used for chaos control: differential evolution, self-organizing migrating algorithm, genetic algorithm, simu- lated annealing and evolutionary strategies in a total of 15 versions. For modeling of spatiotemporal chaos behavior, the so called coupled map lattices were used based on logistic equation to generate chaos. The main aim of this investigation was to show that evolutionary algorithms, under certain conditions, are capable of control- ling of CML deterministic chaos, when the cost function is properly defined along- side the parameters of selected evolutionary algorithms. Investigation consists of four different case studies wi en
utb.faculty Faculty of Applied Informatics
dc.identifier.uri http://hdl.handle.net/10563/1006055
utb.identifier.rivid RIV/70883521:28140/10:63508874!RIV11-GA0-28140___
utb.identifier.obdid 43864810
utb.source c-riv
dc.date.accessioned 2016-04-28T10:37:39Z
dc.date.available 2016-04-28T10:37:39Z
dc.description.sponsorship P(GA102/09/1680), Z(MSM7088352101)
dc.format.extent 521
utb.identifier.utb-sysno 63240
utb.identifier.nkp 5222916
utb.contributor.internalauthor Zelinka, Ivan
riv.obor IN
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