Optimization of local control of chaos by an evolutionary algorithm

Richter, Hendrik; Reinschke, Kurt

doi:10.1016/s0167-2789(00)00080-4

Cited by 65 publications

(29 citation statements)

References 26 publications

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“…A lot of examples about EA's can be easily found. EA's use with chaotic systems is done for example in [17] where EAs has been used on local optimization of chaos, [18] for chaos control with use of the multiobjective cost function or in [19] and [20], where EA's have been studied on chaotic landscapes. Slightly different approach with EA's is presented in [12], selected algorithms were used to synthesize artificial chaotic systems.…”

Section: Motivationmentioning

confidence: 99%

An Investigation on Evolutionary Identification of Continuous Chaotic Systems

Zelinka¹,

Davendra²,

Šenkeřík³

et al. 2011

AIP Conference Proceedings

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This paper discusses the possibility of using evolutionary algorithms for the reconstruction of chaotic systems. The main aim of this work is to show that evolutionary algorithms are capable of the reconstruction of chaotic systems without any partial knowledge of internal structure, i.e. based only on measured data. Algorithm SOMA was used in reported experiments here. Systems selected for numerical experiments here is the well-known Lorenz system. For each algorithm and its version, repeated simulations were done, totaling 20 simulations. According to obtained results it can be stated that evolutionary reconstruction is an alternative and promising way as to how to identify chaotic systems.

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Section: Motivationmentioning

confidence: 99%

An Investigation on Evolutionary Identification of Continuous Chaotic Systems

Zelinka¹,

Davendra²,

Šenkeřík³

et al. 2011

AIP Conference Proceedings

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“…This is very advantageous for successful use of optimization of parameters setting by means of EA, leading to improvement of system behavior and better and faster stabilization to the desired periodic orbits. Some research in this field has been recently done using the evolutionary algorithms for optimization of local control of chaos [12], [13]. The control law in this work is based on the Pyragas method: Extended delay feedback control -ETDAS [14].…”

Section: Introductionmentioning

confidence: 99%

Advanced Targeting Cost Function Design for Evolutionary Optimization of Control of Logistic Equation

Šenkeřík¹,

Zelinka²,

Davendra³

et al. 2010

AIP Conference Proceedings

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This research deals with the optimization of the control of chaos by means of evolutionary algorithms. This work is aimed on an explanation of how to use evolutionary algorithms (EAs) and how to properly define the advanced targeting cost function (CF) securing very fast and precise stabilization of desired state for any initial conditions. As a model of deterministic chaotic system, the one dimensional Logistic equation was used. The evolutionary algorithm Self-Organizing Migrating Algorithm (SOMA) was used in four versions. For each version, repeated simulations were conducted to outline the effectiveness and robustness of used method and targeting CF.

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“…This is very advantageous for successful use of optimization of parameters setting by means of EA, leading to improvement of system behavior and better and faster stabilization to the desired periodic orbits. Some research in this field has been recently done using the evolutionary algorithms for optimization of local control of chaos (Richter and Reinschke, 2000;Richter, 2002). The control law in this work is based on the Pyragas method: Extended delay feedback control -ETDAS (Pyragas 1995).…”

Section: Introductionmentioning

confidence: 99%

Design Of Advanced Targeting Cost Function For Evolutionary Optimization Of Chaos Control

Šenkeřík¹,

Zelinka²,

Oplatková³

2009

ECMS 2009 Proceedings Edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera

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This research deals with the optimization of the control of chaos by means of evolutionary algorithms. The main aim of this work is to show that powerful optimizing tools like evolutionary algorithms can in reality be used for the optimization of deterministic chaos control. This work is aimed on an explanation of how to use evolutionary algorithms (EAs) and how to properly define the advanced targeting cost function (CF) securing very fast and precise stabilization of desired state for any initial conditions. As a model of deterministic chaotic system, the two dimensional Henon map was used. The evolutionary algorithm SelfOrganizing Migrating Algorithm (SOMA) was used in four versions. For each version, repeated simulations were conducted to outline the effectiveness and robustness of used method and targeting CF.

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Optimization of local control of chaos by an evolutionary algorithm

Cited by 65 publications

References 26 publications

An Investigation on Evolutionary Identification of Continuous Chaotic Systems

An Investigation on Evolutionary Identification of Continuous Chaotic Systems

Advanced Targeting Cost Function Design for Evolutionary Optimization of Control of Logistic Equation

Design Of Advanced Targeting Cost Function For Evolutionary Optimization Of Chaos Control

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