数据丢失情况下的最小二乘参数辨识算法

现有系统参数辨识方法大多是建立在输入输出数据可以完全测量和完全获取的基础上,而在实际系统中,由于传感器故障或网络传输机构故障,使得数据丢失现象经常发生。研究一类线性系统在输入或输出数据丢失情况下的系统辨识问题,并将数据丢失现象描述为随机伯努利序列,在此基础上提出新的辨识算法来估计数据丢失情况下系统的参数。最后,通过仿真示例验证所提算法对数据丢失的影响。结果表明,所提出的算法相较于递推最小二乘法有更好的收敛性。

System Identification with Data Dropout

Existing system parameter identification methods are primarily based on the input-output data which are fully available.However,owing to the sensor failure or network transmission mechanism failure in the actual system,data dropout phenomenon often occurres.For system identification problem of a class of linear systems under the condition of input or output data dropout,the data dropout phenomenon is described as a Bernoulli random sequence.And a new algorithm was presented to estimate the parameters with data dropout.Finally,a numerical example validates the effectiveness of the proposed algorithm.The results show that the proposed algorithm has a better convergence than recursive least squares method.