Citation Link: https://doi.org/10.1016/j.ifacol.2023.10.078
Density Estimation in the Regressor Space for Nonlinear System Identification
Publication Type
Article
Subjects
Weighting
Nonlinear system
Kernel (algebra)
Identification (biology)
Kernel density estimation
Computer science
Process (computing)
Space (punctuation)
Inverse
Mathematics
Algorithm
Artificial intelligence
Machine learning
Statistics
Date Issued
2023
Abstract
In this paper, the functioning and performance of a recently proposed weighting approach for dynamic datasets is demonstrated for nonlinear system identification. The overall goal of the new method is to improve data-driven model performance. This is achieved by increasing the weight of training data points in areas of the regressor space that are underrepresented during training, while decreasing the weight for overrepresented data points. The method is based on estimating the parameters of a model using weighted error norms, where the weighting is carried out with the inverse values of the kernel density estimated in the regressor space. The performance of the new method is investigated using nonlinear ARX models for the identification of (i) an artificial Hammerstein process and (ii) a nonlinear real-world process. Overall, the new weighting method shows significantly improved performance on the simulation error. It is demonstrated that performance is improved most notably in areas that are underrepresented in the training data.
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