Novel Hybrid Optimization Algorithm for Parameter Estimation of Chaotic System
Haotian Chang, Jing Feng, Lei Jiang
Available Online March 2017.
- https://doi.org/10.2991/ifmca-16.2017.44How to use a DOI?
- chaotic system, parameter estimation, hybrid optimization algorithm
- This paper proposes a novel hybrid optimization algorithm of Adaptive Cuckoo Search and Particle Swarm Optimization algorithm for parameter estimation of chaotic system. In order to enhance the accuracy and efficiency of ACS, the strategy of exploitation velocity adjustment via acceleration by distance of PSO algorithm is adopted. Thus, the algorithms mentioned above are used for estimation of the parameters of Lorenz chaotic system. Estimation result from each algorithm generates a standard deviation with the true parameter data, which is regarded as the fitness. Compared with ACS and PSO algorithm, the hybrid optimization algorithm is more efficient and accurate for parameter estimation, thus benefitting the simulation and control of chaotic systems.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Haotian Chang AU - Jing Feng AU - Lei Jiang PY - 2017/03 DA - 2017/03 TI - Novel Hybrid Optimization Algorithm for Parameter Estimation of Chaotic System BT - Proceedings of the 2016 International Forum on Mechanical, Control and Automation (IFMCA 2016) PB - Atlantis Press SP - 277 EP - 282 SN - 2352-5401 UR - https://doi.org/10.2991/ifmca-16.2017.44 DO - https://doi.org/10.2991/ifmca-16.2017.44 ID - Chang2017/03 ER -