Reconstruction of measurement data with multiple outliers using novel domain-based RBF

Tianqi Gu, Jun Wang, Dawei Tang, Jian Wang, Tong Guo

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number111385
Number of pages13
JournalMechanical Systems and Signal Processing
Volume214
Early online date30 Mar 2024
DOIs
Publication statusPublished - 15 May 2024

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