A Hybrid Modified PSO System Identification Method Based on the Asynchronous Time-Dependent Learning Factor

DOI

10.2991/caai-17.2017.82How to use a DOI?

Keywords

component; system identification; hammerstein model; particle swarm optimization; simulation

Abstract

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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

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  - Proceedings of the 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  - 10.2991/caai-17.2017.82
ID  - Zhai2017/06
ER  -