A Hybrid Modified PSO System Identification Method Based on the Asynchronous Time-Dependent Learning Factor
Jiangtao Zhai, Chengming Zhu, Chi He, Zhijun Yao, Yuewei Dai
Available Online June 2017.
- https://doi.org/10.2991/caai-17.2017.82How to use a DOI?
- component; system identification; hammerstein model; particle swarm optimization; simulation
- In this paper, the system identification method to Hammerstein model is studied. Considered that the identification accuracy of the standard particle swarm optimization (PSO) is limited and the local optimal problem is easily occurred at later stage, the standard PSO and its initial value setting is firstly discussed. Then, a modified PSO combined with the methods of asynchronous time-varying learning factor and linearly decreasing time-varying weight is put forward to obtain the optimal solution in the whole parameter space. Finally, the comparison experiments are done to verify the accuracy and the advantage of noise resistance of the proposed method.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Jiangtao Zhai AU - Chengming Zhu AU - Chi He AU - Zhijun Yao AU - Yuewei Dai PY - 2017/06 DA - 2017/06 TI - A Hybrid Modified PSO System Identification Method Based on the Asynchronous Time-Dependent Learning Factor BT - 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017) PB - Atlantis Press SP - 362 EP - 365 SN - 1951-6851 UR - https://doi.org/10.2991/caai-17.2017.82 DO - https://doi.org/10.2991/caai-17.2017.82 ID - Zhai2017/06 ER -