李志农, 赵匡, 何况. 核递推最小二乘辨识算法仿真研究[J]. 南昌航空大学学报(自然科学版), 2011, 25(2): 1-6.
引用本文: 李志农, 赵匡, 何况. 核递推最小二乘辨识算法仿真研究[J]. 南昌航空大学学报(自然科学版), 2011, 25(2): 1-6.
LI Zhi-nong, ZHAO Kuang, HE Kuang. Simulation Research of Kernel Recursive Least Square Identification Algorithm[J]. Journal of nanchang hangkong university(Natural science edition), 2011, 25(2): 1-6.
Citation: LI Zhi-nong, ZHAO Kuang, HE Kuang. Simulation Research of Kernel Recursive Least Square Identification Algorithm[J]. Journal of nanchang hangkong university(Natural science edition), 2011, 25(2): 1-6.

核递推最小二乘辨识算法仿真研究

Simulation Research of Kernel Recursive Least Square Identification Algorithm

  • 摘要: 论述了核递推最小二乘辨识思想和三种典型算法即ALD-KRLS、SW-KRLS,和FB-KRLS,通过仿真研究,比较了传统递推最小二乘(RLS)辨识算法和核递推最小二乘(KRLS)辨识算法对非线性系统的辨识能力.仿真研究表明,不论是在辨识精度,稳定性还是抗干扰性方面,KRLS辨识算法明显优于传统RLS辨识法.在这三种典型的KRLS辨识算法,SW-KRLS法比其他两种KRLS辨识算法获得了更好的辨识效果.SW-KRLS法特别适用于时变非线性系统辨识.

     

    Abstract: The kernel recursive least square(KRLS) identification theory and three typical algorithm,i.e.ALD-KRLS,SW-KRLS,FB-KRLS,is discussed.The KRLS algorithm and traditional RLS algorithm is compared for nonlinear system identification ability by the simulation example.The simulation results show that the KRLS identification method is obviously superior to the traditional RLS identification method whether in the identification accuracy,stability or anti-interference.However in the three KRLS identification method,based on the SW-KRLS method has the unique sliding window performance,the SW-KRLS method obtains better identification effect than the ALD-KRLS and FB-KRLS method.the SW-KRLS identification method is particularly suitable time-varying nonlinear system identification.

     

/

返回文章
返回