A novel APSO-aided weighted LSSVM method for nonlinear hammerstein system identification

作者：Ma, Liang; Liu, Xinggao^*

来源：Journal of the Franklin Institute, 2017, 354(4): 1892-1906.

DOI：10.1016/j.jfranklin.2016.12.022

• APSOWLSVM
• Hammerstein system
• SVM
• RLS
• Parameter estimation

Identification of Hammerstein nonlinear models has received much attention due to its ability to describe a wide variety of nonlinear systems. A novel identification approach based on intelligent optimal weighted least squares SVM (WLSSVM) is proposed for Hammerstein system, where a new adaptive particle swarm optimization algorithm (APSO) using the evolutionary state estimation technique and mutation operator is applied. The proposed method not only has fast convergence to the global optimal solution but also has good identification results. The comparison researches are carried out among the proposed method, WLSSVM, LSSVM and RIV methods in detail. The research results show the effectiveness of proposed APSO-WLSSVM method.

• 出版日期2017-3
• 单位浙江大学; 工业控制技术国家重点实验室