TY - JOUR
T1 - Reconstruction of measurement data with multiple outliers using novel domain-based RBF
AU - Gu, Tianqi
AU - Wang, Jun
AU - Tang, Dawei
AU - Wang, Jian
AU - Guo, Tong
N1 - Funding Information:
This work was supported by the National Natural Science Foundation of China (Grant No. 51605094 and 52075206), Natural Science Foundation of Fujian Province (Grant No. 2021J01562) and Hubei Provincial Key Research Program (Grant No. 2021BAA056).
Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/5/15
Y1 - 2024/5/15
N2 - Due to the high accuracy of computation, radial basis function (RBF) is widely recognized as a versatile and effective method for interpolating and approximating discrete points in various fields. However, RBF is quite sensitive to outliers, which can easily lead to distorted results. In this article, a novel overlapped domain-based RBF (ODRBF) method is proposed, in which the concept of effective domain is introduced to build a moving model, and Student's t-regression and Gaussian mixture model (GMM) clustering are used for dealing with local anomalies. By introducing the effective domain, the estimated points and domain radius are constructed and the global model can be transformed into local estimation models. In each effective domain, a series of estimation models are iteratively generated through Student's t-regression, and based on the distances between the estimation model and discrete points, GMM clustering is used to subsequently select the data as the input of the next regression. This iterative strategy in each effective domain ensures the removal of multiple outliers. Then, the preserved points in the processed effective domain are used to obtain local estimated value by RBF. The proposed method demonstrates strong robustness to highly contaminated dataset in the reconstruction of the simulation and experimental datasets.
AB - Due to the high accuracy of computation, radial basis function (RBF) is widely recognized as a versatile and effective method for interpolating and approximating discrete points in various fields. However, RBF is quite sensitive to outliers, which can easily lead to distorted results. In this article, a novel overlapped domain-based RBF (ODRBF) method is proposed, in which the concept of effective domain is introduced to build a moving model, and Student's t-regression and Gaussian mixture model (GMM) clustering are used for dealing with local anomalies. By introducing the effective domain, the estimated points and domain radius are constructed and the global model can be transformed into local estimation models. In each effective domain, a series of estimation models are iteratively generated through Student's t-regression, and based on the distances between the estimation model and discrete points, GMM clustering is used to subsequently select the data as the input of the next regression. This iterative strategy in each effective domain ensures the removal of multiple outliers. Then, the preserved points in the processed effective domain are used to obtain local estimated value by RBF. The proposed method demonstrates strong robustness to highly contaminated dataset in the reconstruction of the simulation and experimental datasets.
KW - Effective domain
KW - Gaussian mixture model clustering
KW - Radial basis function
KW - Student's t-distribution
UR - http://www.scopus.com/inward/record.url?scp=85189525000&partnerID=8YFLogxK
U2 - 10.1016/j.ymssp.2024.111385
DO - 10.1016/j.ymssp.2024.111385
M3 - Article
AN - SCOPUS:85189525000
VL - 214
JO - Mechanical Systems and Signal Processing
JF - Mechanical Systems and Signal Processing
SN - 0888-3270
M1 - 111385
ER -