Abstract: The measured data inevitably contain abnormal data under the normal operating conditions. Most of the existing algorithms, such as least squares identification and maximum likelihood estimation, are easily affected by abnormal data and appear large indentation deviation. It is a difficult task needed to be addressed that how to improve the sensitivity of the existing algorithm or build a new parameter identifying algorithm with outlier-tolerance ability to abnormal data in system identification technology application. In this paper, the sensitivity of the RML to the sampled abnormal data was analyzed and a new improvement algorithm of CAR process is established to improve outlier-tolerance ability of the RML identification when there are outliers in the sampling series. The improved algorithm not only effectively inhibits the negative impact of the abnormal data but also effectively improve the quality of the parameter identification results. Some simulation given in this paper shows that the improved RML algorithm has strong outlier-tolerance. This paper’s research results play an important role in engineering control, signal processing, industrial automation and aerospace or other fields.

Hong Teng-teng and Hu Shaolin, “Outlier-Tolerance RML Identification of Parameters in CAR Model” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 5(5), 2016. http://dx.doi.org/10.14569/IJARAI.2016.050502

@article{Teng-teng2016,
title = {Outlier-Tolerance RML Identification of Parameters in CAR Model},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2016.050502},
url = {http://dx.doi.org/10.14569/IJARAI.2016.050502},
year = {2016},
publisher = {The Science and Information Organization},
volume = {5},
number = {5},
author = {Hong Teng-teng and Hu Shaolin}
}