摘要
针对含有过程噪声的Hammerstein-Wiener模型,提出一种偏差补偿递推最小二乘辨识方法.通过将偏差补偿引入到递推最小二乘算法中,在线辨识包含原系统参数乘积项的参数向量.并用鞅收敛定理证明偏差补偿递推最小二乘辨识算法的收敛性,分析表明在持续激励的条件下参数估计偏差一致收敛于零.仿真结果表明该方法优于递推最小二乘辨识方法.
A bias-compensated recursive least squares（BCRLS） algorithm was proposed to identify the Hammerstein-Wiener model with process noise,i.e.,the bias compensation was introduced into the recursive least squares algorithm.The parameter vector containing the product of the original system parameters was thus identified on-line.Then,the convergence of the algorithm was proved by the martingale convergence theorem.It was found that the estimation error of parameter product uniformly converges to zero during persistent excitation.Simulation results showed that the proposed method is better than the identification method by recursive least squares.
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2010年第4期469-472,476,共5页
Journal of Northeastern University(Natural Science)
基金
国家高技术研究发展计划项目(2007AA04Z194
2007AA041401)
