| Title | Weighted LS-SVMR-Based System Identification with Outliers |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Ma, Congjun, Wang, Haipeng, Zhao, Tao, Dian, Songyi |
| Conference Name | Proceedings of the 2019 4th International Conference on Automation, Control and Robotics Engineering |
| Date Published | jul |
| Publisher | Association for Computing Machinery |
| Conference Location | Shenzhen, China |
| ISBN Number | 978-1-4503-7186-5 |
| Keywords | composability, Identification, LS-SVM, Outliers, Predictive Metrics, pubcrawl, Resiliency, Support vector machines, Weighted LS-SVMR |
| Abstract | Plenty of methods applied in system identification, while those based on data-driven are increasingly popular. Usually we ignore the absence of outliers among the system to be modeled, but it is unreachable in reality. To improve the precision of identification towards system with outliers, advantageous approaches with robustness are needed. This study analyzes the superiority of weighted Least Square Support Vector Machine Regression (LS-SVMR) in the field of system identification under random outliers, and compare it with LS-SVMR mainly. |
| URL | https://doi.org/10.1145/3351917.3351940 |
| DOI | 10.1145/3351917.3351940 |
| Citation Key | ma_weighted_2019 |
Weighted LS-SVMR-Based System Identification with Outliers