摘要
在有限采样情况下,研究具有时滞的多输入单输出受控自回归系统的参数辨识和时滞估计问题.当采样次数少于未知变量数时,描述系统的方程组是欠定的,对其目标函数求解是NP-hard问题,传统方法无法有效辨识出系统参数.受压缩感知理论的启发,基于参数向量所具有的稀疏特性,提出一种新的阈值正交匹配追踪算法辨识系统的参数和时滞.仿真实验表明,所提出的算法能在少量采样时有效地辨识系统参数、估计未知时滞,同时验证了算法的有效性.
The identification problem of the multiple-input single-output controlled autoregressive systems with unknown time-delays and finite sampled data is studied. A linear equation set is said to be undetermined if the number of equations is less than that of unknown variables. Traditional methods such as the least squares algorithm cannot provide the unique solution for an undetermined equation set because it is an NP-hard problem. Based on the compressed sensing theory, a new orthogonal matching pursuit（OMP） algorithm is proposed for estimating the time-delays of the input channels and the system parameters. The simulation results show the effectiveness of the proposed method.
出处
《控制与决策》
EI
CSCD
北大核心
2015年第11期2103-2107,共5页
Control and Decision
基金
国家自然科学基金项目(61304138
61473136
61203111)
江苏省自然科学基金项目(BK20130163)
关键词
压缩感知
稀疏
参数辨识
时滞估计
正交匹配追踪算法
compressed sensing
sparse
parameter estimation
time-delay estimation
orthogonal matching pursuit algorithm
