基于辅助模型和数据滤波的伪线性回归系统参数估计方法  

Parameter estimation methods for pseudo-linear regressive systems based on auxiliary model and data filtering

作　　者：丁盛[1] 

机构地区：[1]江南大学物联网工程学院,江苏无锡214122

出　　处：《计算机应用》2014年第1期236-238,254,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60973043)

摘　　要：针对伪线性输出误差回归系统的辨识模型新息信息向量存在不可测变量的问题,首先通过构造一个辅助模型,用辅助模型的输出代替未知中间变量,推导得到的基于辅助模型的递推最小二乘参数估计算法计算量较大,但算法的辨识效果不佳。进一步采用估计的噪声模型对系统观测数据进行滤波,使用滤波后的数据进行参数估计,从而推导提出了基于数据滤波的递推最小二乘参数估计算法。仿真结果表明,所提算法能够有效估计伪线性回归线性输出误差系统的参数。For the pseudo-linear output elTorregressive systems whose identification model has the unknown variables in the information vector, this paper presented an auxiliary model based recursive least squares parameter estimation algorithm that was derived through constructing an auxiliary model and replacing the unknown inner variables with the outputs of the auxiliary model, but the effect was not good. Furthermore, through filtering the observation data with the estimated transfer function of the noise model and using the filtered data to estimate the parameters, the authors presented a data filtrating based recursive least squares parameter estimation algorithm. The simulation results show that the proposed algorithm can estimate the parameters of pseudo-linear output errorregressive systems effectively.

关 键 词：参数估计 最小二乘 伪线性系统 数据滤波 辅助模型 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]