Accuracy and stability of Artificial Neural Networks for HP-Splines frequency parameter selection
For researchers in signal processing and spline-based regression, this work provides a data-driven method to automate parameter selection, though it is an incremental improvement over existing techniques.
The paper proposes using artificial neural networks to stably and data-adaptively select the frequency parameter in HP-splines, achieving high accuracy and stable performance in numerical experiments.
This paper explores the use of artificial neural networks for the stable and data-driven selection of the frequency parameter in hyperbolic polynomial penalized splines (HP-splines). This parameter defines the underlying spline space and is essential for adapting the model to exponential patterns in the data, such as those encountered in signal processing. The theoretical approximation properties of deep neural network architectures are investigated to establish a connection between classical spline-based regression and modern data-driven learning methods. Based on this analysis, a neural network is designed to predict optimal HP-spline parameters by balancing approximation accuracy, stability analysis, and complexity control, thereby producing neural architectures that are both expressive and stable. Numerical experiments confirm that the proposed approach achieves both high accuracy and stable performance, validating the theoretical findings.