Data Mining Algorithm for Off-Group Points on Noise Polluted Time Series Based on ESO

The practically measured signals always contain wild values deviating far from true values. How to remove these wild values is an important research project for data mining of off-group points. In active disturbance rejection controller (ADRC), it is difficult to acquire accurate signal, since the signal is vulnerable to the influence of the wild values. Therefore, this paper puts forward extend state observer (ESO) algorithm to replace tracking differentiator (TD) method. The performances are compared between them under equal conditions and for different rang of wild values. The result suggests that the ESO algorithm will remove the wild values effectively and better, when it's relatively small.