Journal of System Simulation
Abstract
Abstract: In order to accurately estimate the unknown parameters for fractional order chaotic systems, a quantum particle swarm optimization algorithm based on differential quantum properties is proposed. On the basis of quantum behaved particle swarm optimization, variation, crossover and selection operation are utilized by particles, which can better keep the diversity of the particles in the population, avoiding the local optimum in the later phase of the iteration. The multi-neighborhood local search strategy is used for particles’ local search to improve search precision. Standard test functions are used to test the algorithm, and the test results show that the algorithm has good global search capability. At last, the proposed algorithm is applied in the parameter estimation for fractional-order Lorenz system and fractional-order Chen system, and the estimation results demonstrate that the algorithm is effective and robust.
Recommended Citation
Ze, Dong and Ning, Ma
(2019)
"Differential Evolution Quantum Particle Swarm Optimization for Parameter Estimation of Fractional-order Chaotic System,"
Journal of System Simulation: Vol. 31:
Iss.
8, Article 20.
DOI: 10.16182/j.issn1004731x.joss.17-0265
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol31/iss8/20
First Page
1664
Revised Date
2017-09-20
DOI Link
https://doi.org/10.16182/j.issn1004731x.joss.17-0265
Last Page
1673
CLC
TP273
Recommended Citation
Dong Ze, Ma Ning. Differential Evolution Quantum Particle Swarm Optimization for Parameter Estimation of Fractional-order Chaotic System[J]. Journal of System Simulation, 2019, 31(8): 1664-1673.
DOI
10.16182/j.issn1004731x.joss.17-0265
Included in
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons