Parameter estimation for fractional-order chaotic systems by improved bird swarm optimization algorithm

2019 ◽  
Vol 30 (11) ◽  
pp. 1950086 ◽  
Author(s):  
Pei Zhang ◽  
Renyu Yang ◽  
Renhuan Yang ◽  
Gong Ren ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Fractional Order ◽  
Chaotic Systems ◽  
Dimensional Parameter ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Unified Chaotic System ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm ◽  
Control And Synchronization

The essence of parameter estimation for fractional-order chaotic systems is a multi-dimensional parameter optimization problem, which is of great significance for implementing fractional-order chaos control and synchronization. Aiming at the parameter estimation problem of fractional-order chaotic systems, an improved algorithm based on bird swarm algorithm is proposed. The proposed algorithm further studies the social behavior of the original bird swarm algorithm and optimizes the foraging behavior in the original bird swarm algorithm. This method is applied to parameter estimation of fractional-order chaotic systems. Fractional-order unified chaotic system and fractional-order Lorenz system are selected as two examples for parameter estimation systems. Numerical simulation shows that the algorithm has better convergence accuracy, convergence speed and universality than bird swarm algorithm, artificial bee colony algorithm, particle swarm optimization and genetic algorithm.

Parameter estimation for chaotic systems using improved bird swarm algorithm

2017 ◽  
Vol 31 (36) ◽  
pp. 1750346 ◽  
Author(s):  
Chuangbiao Xu ◽  
Renhuan Yang
Keyword(s):  
Parameter Estimation ◽  
Optimization Problem ◽  
Chaotic Systems ◽  
Lorenz System ◽  
Learning Strategy ◽  
Research Fields ◽  
Simulation Results ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm ◽  
The Lorenz System

Parameter estimation of chaotic systems is an important problem in nonlinear science and has aroused increasing interest of many research fields, which can be basically reduced to a multidimensional optimization problem. In this paper, an improved boundary bird swarm algorithm is used to estimate the parameters of chaotic systems. This algorithm can combine the good global convergence and robustness of the bird swarm algorithm and the exploitation capability of improved boundary learning strategy. Experiments are conducted on the Lorenz system and the coupling motor system. Numerical simulation results reveal the effectiveness and with desirable performance of IBBSA for parameter estimation of chaotic systems.

scholarly journals The Robust Control and Synchronization of a Class of Fractional-Order Chaotic Systems with External Disturbances via a Single Output

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Runzi Luo ◽  
Haipeng Su
Keyword(s):  
Robust Control ◽  
Fractional Order ◽  
Chaos Control ◽  
Chaotic Systems ◽  
Sufficient Conditions ◽  
External Disturbances ◽  
Numerical Examples ◽  
Single Output ◽  
Lorenz Chaotic System ◽  
Control And Synchronization

This paper investigates the stabilization and synchronization of a class of fractional-order chaotic systems which are affected by external disturbances. The chaotic systems are assumed that only a single output can be used to design the controller. In order to design the proper controller, some observer systems are proposed. By using the observer systems some sufficient conditions for achieving chaos control and synchronization of fractional-order chaotic systems are derived. Numerical examples are presented by taking the fractional-order generalized Lorenz chaotic system as an example to show the feasibility and validity of the proposed method.

A Modified PSO Algorithm for Parameters Identification of the Double-Dispersion Cole Model

2018 ◽  
Vol 27 (13) ◽  
pp. 1850210 ◽  
Author(s):  
Lu Liu ◽  
Liang Shan ◽  
Chao Jiang ◽  
Yue-Wei Dai ◽  
Cheng-Lin Liu ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Fractional Order ◽  
Electric Power System ◽  
Parameters Identification ◽  
Fractional Order System ◽  
Order System ◽  
Dimensional Parameter ◽  
Position Information ◽  
Double Dispersion ◽  
Cole Model

Many practical systems, such as thermal system, economic system and electric power system, can be more accurately described by the fractional-order system rather than integer-order system. Therefore, it is an important topic to study the fractional-order system and estimate its parameters. The problem of parameter estimation is essentially a multi-dimensional parameter optimization problem. In this paper, according to the average value of position information, an improved Tent mapping and a piecewise mutation probability, a modified particle swarm optimization (MPSO) algorithm is presented to solve the parameter estimation problem. The performance of MPSO is tested with eight benchmark functions, which proves the effectiveness of the algorithm. Based on the double-dispersion Cole model, the proposed MPSO algorithm is used to estimate the parameters for the generated simulated datasets. Experimental results show that the MPSO algorithm for parameters identification of the Cole model is an effective and promising method with high accuracy and good robustness.

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