Mutual synchronization and control between artificial chaotic system and human

2018 ◽  
Author(s):  
Dobromir Dotov ◽  
Tom Froese
Keyword(s):  
Chaotic System ◽  
Mutual Synchronization ◽  
And Control

scholarly journals Morphological Analysis for Three-Dimensional Chaotic Delay Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Yusong Lu ◽  
Ricai Luo ◽  
Yongfu Zou
Keyword(s):  
Neural Networks ◽  
Chaotic System ◽  
Morphological Analysis ◽  
Chaotic Behavior ◽  
Three Dimensional ◽  
Hopfield Neural Network ◽  
Coefficient Matrix ◽  
Initial Value ◽  
The Neural Networks ◽  
And Control

The study focuses on the chaotic behavior of a three-dimensional Hopfield neural network with time delay. We find the aspecific coefficient matrix and the initial value condition of the system and use MATLAB software to draw its graph. The result shows that their shape is very similar to the figure of Roslerʼs chaotic system. Furthermore, we analyzed the divergence, the eigenvalue of the Jacobian matrix for the equilibrium point, and the Lyapunov exponent of the system. These properties prove that the system does have chaotic behavior. This result not only confirms that there is chaos in the neural networks but also that the chaotic characteristics of the system are very similar to those of Roslerʼs chaotic system under certain conditions. This discovery provides useful information that can be applied to other aspects of chaotic Hopfield neural networks, such as chaotic synchronization and control.

Fractional High-Order Differential Estimator and Feedback Controller Design for a Single-Input–Single-Output Affine Chaotic System

2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Erdinc Sahin ◽  
Mustafa Sinasi Ayas
Keyword(s):  
Chaotic System ◽  
High Order ◽  
Feedback Controller ◽  
Control Problems ◽  
Single Input Single Output ◽  
Single Output ◽  
System Output ◽  
Single Input ◽  
Estimation And Control ◽  
And Control

Abstract Control of chaos generally refers to realize a desired behavior of chaotic system output and its states. In this manner, we design a fractional high-order differential feedback controller (FHODFC) to increase tracking performance of a nonlinear system output and its differentials for a desired trajectory signal. The proposed controller is based on fractional calculus and high-order extracted differentials of error signal. The suggested fractional approach is applied to a single-input–single-output affine Duffing-Holmes dynamical system in matlab/simulink environment. Duffing-Holmes system is analyzed for two different problems: estimation and control problems. The simulation results clearly demonstrate superior dynamic behavior of the FHODFC compared to the classical high-order differential feedback controller (HODFC) version for both estimation and control problems.

Duffing Chaotic System Stability Control Based on Sliding Mode Control

2012 ◽  
Vol 605-607 ◽  
pp. 1639-1642
Author(s):  
Ding Ma
Keyword(s):  
Chaotic System ◽  
Control Method ◽  
Sliding Mode ◽  
Variable Structure ◽  
Stability Control ◽  
System Stability ◽  
Terminal Sliding Mode ◽  
Sliding Mode Variable Structure ◽  
The Stability ◽  
And Control

Considering the Duffing chaotic system, the problem of stability control based on the terminal sliding mode variable structure is studied. A new terminal sliding mode surface and control law are designed. On this basis, the stability of closed-loop system is analyzed. Simulation results show the effectiveness of the control method.

CONTROLLING CHAOS WITHOUT FEEDBACK AND CONTROL SIGNALS

1993 ◽  
Vol 03 (02) ◽  
pp. 459-468 ◽  
Author(s):  
T. KAPITANIAK ◽  
LJ. KOCAREV ◽  
L.O. CHUA
Keyword(s):  
Experimental Study ◽  
Chaotic System ◽  
Analytical Study ◽  
Simple System ◽  
Electrical System ◽  
Controlling Chaos ◽  
Chua's Circuit ◽  
Control Signals ◽  
And Control ◽  
Illustrative Application

We describe an effective method for controlling chaos by coupling a main chaotic system to a new but simple system with easily changeable parameters. The method is applied to Duffing’s oscillator (numerical and analytical study) and to Chua’s circuit (experimental study). The effectiveness of our method in controlling noisy systems, as well as an illustrative application in a mechanical and an electrical system, are discussed.

FUZZY PREDICTIVE CONTROL OF UNCERTAIN CHAOTIC SYSTEMS USING TIME SERIES

1999 ◽  
Vol 09 (04) ◽  
pp. 757-767 ◽  
Author(s):  
LIANG CHEN ◽  
GUANRONG CHEN
Keyword(s):  
Chaotic System ◽  
Controller Design ◽  
Recursive Least Squares ◽  
Input Output ◽  
Output Data ◽  
Lyapunov Stability Criterion ◽  
System Input ◽  
Prediction And Control ◽  
And Control ◽  
Fuzzy Predictive Control

In this paper, a simple fuzzy logic based intelligent mechanism is developed for predicting and controlling a chaotic system to a desired target, using only input–output data obtained from the unknown (or uncertain) underlying chaotic system. In the chaos prediction phase, a fuzzy system approach incorporating with Gaussian type of fuzzy membership functions is used. Only system input–output data are needed for prediction, and a recursive least-squares computational algorithm is employed for the calculation. In the controller design phase, the Lyapunov stability criterion is used, which forms the basis of the main design principle. Some simulation results on the chaotic Sin map and Hénon map are given, for both prediction and control, to illustrate the effectiveness and control performance of the proposed method.

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