摘要
讨论由随机微分方程描述的随机连续信号的辨识建模问题。提出并证明了非平稳的连续Wiener过程通过稳定的连续线性系统后为平稳随机过程 ,且均值和自相关函数阵为时间遍历的。基于状态空间分析 ,给出了连续随机信号建模的时间序列分析方法 ,并证明了参数估计的一致收敛性。
The modeling problem for stochastic continuous signals, described by stochastic differential equations, is discussed. It is proved that the stochastic process produced by a non stationary continuous Wiener process and a stable linear filter is stationary and its expectation and self correlation function are ergodic. Based on the state space analysis, the time series analysis method for identification of the stochastic continuous signals, proved as consistent convergence, is given. Simulation results show the effectiveness of the proposed method.
出处
《控制与决策》
EI
CSCD
北大核心
2000年第4期395-400,共6页
Control and Decision
基金
中国科学院机器人开放实验室开放课题基金项目
关键词
随机连续信号
时间序列分析
建模
WIENER过程
stochastic continuous signal, time-series analysis, modeling,system identification, Wiener process
