Control and synchronization of chaotic systems by differential evolution algorithm

2007 ◽  
Vol 34 (2) ◽  
pp. 412-419 ◽  
Author(s):  
Bo Liu ◽  
Ling Wang ◽  
Yi-Hui Jin ◽  
De-Xian Huang ◽  
Fang Tang
Keyword(s):  
Differential Evolution ◽  
Chaotic Systems ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm ◽  
Control And Synchronization

Output Tracking Control and Synchronization of Continuous Chaotic Systems Using Differential Evolution Algorithm

2010 ◽  
Vol 37-38 ◽  
pp. 823-828
Author(s):  
Shu Bo Liu ◽  
Shu Min Zhou ◽  
Li Yong Hu
Keyword(s):  
Differential Evolution ◽  
Tracking Control ◽  
Chaotic Systems ◽  
Differential Evolution Algorithm ◽  
Theoretical Method ◽  
Parameter Mismatch ◽  
Single Input Single Output ◽  
Output Tracking ◽  
Output Tracking Control ◽  
Control And Synchronization

This paper applies differential evolution (DE) algorithm to realize the output tracking control and synchronization of continuous chaotic systems. The output tracking control of single-input single-output (SISO) and multi-input multi-output (MIMO) chaotic system is investigated. Moreover, synchronization of chaotic systems with parameter mismatch or structure difference is also under discussion. Numerical simulations based on the well-known models such as Lorenz and Chen systems are used to illustrate the validity of this theoretical method.

scholarly journals An Alternate Iterative Differential Evolution Algorithm for Parameter Identification of Chaotic Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Wanli Xiang ◽  
Xuelei Meng ◽  
Meiqing An
Keyword(s):  
Parameter Estimation ◽  
Chaotic Systems ◽  
Differential Evolution Algorithm ◽  
Continuous Optimization ◽  
Population Diversity ◽  
Evolution Algorithm ◽  
Mutation Strategies ◽  
Control And Synchronization ◽  
Global Continuous Optimization ◽  
Better Than

Parameter estimation of chaotic systems plays a key role for control and synchronization of chaotic systems. At first, the parameter estimation of chaotic systems is mathematically formulated as a global continuous optimization problem. Then through integrating two differential mutation strategies, an improved greedy selection mechanism and a population diversity balance scheme, an alternate iterative differential algorithm, called AIDE, is presented to solve the problem in this paper. Subsequently, experiments are tested on a set of cases of parameter estimation of chaotic systems and the results show that AIDE is better than or at least equal to DE/rand/1/bin, DE/best/1/bin, and other four well-known algorithms in all cases.

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