Adaptive control and identification of chaotic systems

2001 ◽  
Vol 10 (6) ◽  
pp. 494-496 ◽  
Author(s):  
Li Zhi ◽  
Han Chong-zhao
Keyword(s):  
Adaptive Control ◽  
Chaotic Systems

scholarly journals Universal Projective Synchronization of Two Different Hyperchaotic Systems with Unknown Parameters

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Baojie Zhang ◽  
Hongxing Li
Keyword(s):  
Adaptive Control ◽  
Chaotic Systems ◽  
Control Method ◽  
Scaling Function ◽  
Lyapunov Stability Theory ◽  
Projective Synchronization ◽  
Reference Systems ◽  
Unknown Parameters ◽  
Hyperchaotic Systems ◽  
Function Matrix

Universal projective synchronization (UPS) of two chaotic systems is defined. Based on the Lyapunov stability theory, an adaptive control method is derived such that UPS of two different hyperchaotic systems with unknown parameters is realized, which is up to a scaling function matrix and three kinds of reference systems, respectively. Numerical simulations are used to verify the effectiveness of the scheme.

Adaptive Control for Linearizable Chaotic Systems

2006 ◽  
Vol 12 (2) ◽  
pp. 119-137 ◽  
Author(s):  
S. Bowong ◽  
A. Temgoua Kagou
Keyword(s):  
Adaptive Control ◽  
Chaotic Systems

Adaptive control for a class of nonlinear chaotic systems with quantized input delays

2020 ◽  
Vol 357 (1) ◽  
pp. 254-278
Author(s):  
Mostafa Asadollahi ◽  
Amir Rikhtehgar Ghiasi ◽  
Mohammad Ali Badamchizadeh
Keyword(s):  
Adaptive Control ◽  
Chaotic Systems ◽  
Input Delays

Synchronization of a Network of Chaotic Neurons Using Adaptive Control in Noisy Environment

1998 ◽  
Vol 08 (09) ◽  
pp. 1821-1830 ◽  
Author(s):  
Bernard Cazelles
Keyword(s):  
Adaptive Control ◽  
Kalman Filter ◽  
Chaotic Systems ◽  
Control Mechanism ◽  
Control Method ◽  
Rhythmic Activity ◽  
Noisy Environment ◽  
Network Switch ◽  
Clustering Behavior

The synchronization of chaotic systems have received an increasing interest in the last few years. In an attempt to understand some of the possible mechanisms of synchronization of neurons in a noisy environment, the present study extends a control method which is based on the Kalman filter. This adaptive control mechanism is able to modulate the frequency and the clustering behavior of a network of neurons and can thus make the network switch dynamically to different rhythmic activity, leading to different coding possiblity.

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