基于子空间跟踪的状态空间系统辨识方法及其在时变系统辨识中的应用  

Subspace Tracking in State Space Model System Identification with Applications in Time-Varying System Identification

作　　者：马革新[1] 张大力[1] 李衍达[1] 

机构地区：[1]清华大学自动化系

出　　处：《信号处理》1998年第4期346-352,共7页Journal of Signal Processing

摘　　要：基于系统状态空间模型的系统辨识方法的一个困难在于算法具有较大的运算量和存储量。本文将基于APEX算法的子空间跟踪方法引入辨识算法。APEX算法的神经网络实现可以有效地减少辨识算法的运算量和存储量。本文将得到的算法应用于时变系统的辨识，仿真结果和应用于实际数据的结果验证了方法的有效性。A dificulty in state spase model system identification approaches is the high computation and storage burden. In this paper, a subspace tracking approach based on APEX algorithm is applied in state space model identification. The Neural network model of APEX algorithm can effectively reduce the computation cost andmake the state spce model identification algorithm available for real time application. We apply the state spacemodel identification algorithm obtained in time varying system identification.This approach is demonstrated in thesimulation and application in real data.

关 键 词：子空间跟踪 神经网络 时变系统 系统辨识 

分 类 号：O235[理学—运筹学与控制论]