摘要
针对非线性系统的辨识问题,提出了非线性压缩测量辨识算法,且推导出了一种符合压缩感知测量准则的测量模型。相比递归最小二乘法,该方法极大地减少了所需的测量数,使得高阶Volterra级数辨识成为可能。此外,还分析了实际应用中的各项因素对辨识准确性的影响,如信号稀疏度、测量噪声、测量矩阵形式等。
For the identification problem of nonlinear systems,the accuracy and stability of the nonlinear compression measurement identification algorithm were proved in the simulation experiment,and the complete signal was obtained accurately only by using constant multiple measurement times of the signal sparsity. Compared with the least square method,the proposed algorithm has greatly reduced the needed measurements,therefore,it is possible for the identification of high-order Volterra series. Furthermore,the influence of all factors on the accuracy of system identification was analyzed,such as signal sparsity,measurement noise,measurement matrix form,etc.
作者
邱棚
姚旭日
李鸣谦
翟光杰
QIU Peng;YAO Xuri;LI Mingqian;ZHAI Guangjie(National Space Science Center,Chinese Academy of Sciences,Beijing 100190,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《国防科技大学学报》
EI
CAS
CSCD
北大核心
2020年第1期125-132,共8页
Journal of National University of Defense Technology
基金
国家自然科学基金资助项目(61605218)
中国科学院国防创新基金资助项目(CXJJ-17S023)
作者简介
邱棚(1991—),男,北京人,博士研究生,E-mail:qpeng0504@163.com;通信作者:翟光杰,男,教授,博士,博士生导师,E-mail:gjzhai@nssc.ac.cn。
