Model-free chaos control in a chaotic Henon-like system using Takens embedding theory

2017 ◽  
Author(s):  
Reza Hajiloo ◽  
Hassan Salarieh ◽  
Aria Alasty
Keyword(s):  
Chaos Control ◽  
Embedding Theory ◽  
Model Free ◽  
Takens Embedding

Control of Continuous Time Chaotic Systems With Unknown Dynamics and Limitation on State Measurement

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Hojjat Kaveh ◽  
Hassan Salarieh
Keyword(s):  
Chaotic Attractor ◽  
Chaos Control ◽  
Chaotic Systems ◽  
Control Method ◽  
Original System ◽  
Control Of Chaos ◽  
State Measurement ◽  
New Chaotic Attractor ◽  
Systematic Control ◽  
Takens Embedding

This paper has dedicated to study the control of chaos when the system dynamics is unknown and there are some limitations on measuring states. There are many chaotic systems with these features occurring in many biological, economical and mechanical systems. The usual chaos control methods do not have the ability to present a systematic control method for these kinds of systems. To fulfill these strict conditions, we have employed Takens embedding theorem which guarantees the preservation of topological characteristics of the chaotic attractor under an embedding named “Takens transformation.” Takens transformation just needs time series of one of the measurable states. This transformation reconstructs a new chaotic attractor which is topologically similar to the unknown original attractor. After reconstructing a new attractor its governing dynamics has been identified. The measurable state of the original system which is one of the states of the reconstructed system has been controlled by delayed feedback method. Then the controlled measurable state induced a stable response to all of the states of the original system.

Model-free chaos control based on AHGSA for a vibro-impact system

2018 ◽  
Vol 94 (2) ◽  
pp. 845-855 ◽  
Author(s):  
Xiao-juan Wei ◽  
Ning-zhou Li ◽  
Wang-cai Ding ◽  
Cao-hui Zhang
Keyword(s):  
Chaos Control ◽  
Impact System ◽  
Model Free

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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