摘要
对于输出误差模型描述的多输入单输出系统,辨识的困难在于辨识模型信息向量中包含系统未知输出量(真实输出或无噪输出),以致标准辨识算法无法应用.提出了利用输出估计代替系统真实输出的辨识思想,即通过估计模型预测(估算)系统输出,利用这个估计输出来递推计算系统参数,进而提出了基于输出估计的随机梯度辨识算法,并研究了算法的收敛性,给出了仿真例子.
For multiple-input single-output output-error systems,a difficulty is that the information vector in the identification model contains the unknown system outputs （ true outputs or noise-free outputs） ,thus the standard identification algorithm cannot be applied directly. This paper presents a stochastic gradient identification algorithm based on the unknown output estimation. The basic idea is to replace the true output with the output estimate which is predicted/estimated by the estimated model,and also to compute the system parameter estimates by using the output estimates. Convergence of the proposed algorithm is studied and a simulation example is provided.
出处
《南京信息工程大学学报（自然科学版）》
CAS
2010年第6期481-488,共8页
Journal of Nanjing University of Information Science & Technology（Natural Science Edition）
基金
国家自然科学基金(60974043)
关键词
系统辨识
递推辨识
参数估计
随机梯度
多新息辨识
多变量系统
system identification
recursive identification
parameter estimation
stochastic gradient
multi-innovation identification
multivariable systems
