@inproceedings{90fc47db21764f3f8bb9df78bea59cb3,
title = "Gaussian and Non-Gaussian Noise Effects on Data-Driven Modeling: A Comparative Investigation",
abstract = "To ensure reliable predictions for various applications, it is essential to understand the impact of Gaussian and non-Gaussian noise on data-driven modeling. Gaussian noise has been a convenient assumption for model development, but real-world scenarios sometimes defy Gaussian assumptions. Our research compared the performances of the direct and parametric data-driven modeling methods with Gaussian and non-Gaussian sensor noise. The direct data-driven modeling methods that are evaluated are direct data-driven methods with pulse input-output dataset and direct data-driven methods with step input-output dataset. The parametric models that are evaluated are the Output Error (OE), Autoregressive with Moving Average and Exogenous Input (ARMAX), Autoregressive with Exogenous Input (ARX) and Box-Jenkins model (BJ). The result shows that the performance of the direct and the parametric data-driven modeling methods deteriorates under non-Gaussian noise.",
keywords = "Data-driven modeling, Gaussian Noise, Model performance, non-Gaussian Noise",
author = "Mersha, \{Bemnet Wondimagegnehu\} and Yaping Dai and Kaoru Hirota",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; 11th International Symposium on Computational Intelligence and Industrial Applications, ISCIIA 2024 ; Conference date: 01-11-2024 Through 05-11-2024",
year = "2025",
doi = "10.1007/978-981-96-4753-8\_1",
language = "English",
isbn = "9789819647521",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1--13",
editor = "Bin Xin and Hongbin Ma and Jinhua She and Weihua Cao",
booktitle = "Computational Intelligence and Industrial Applications - 11th International Symposium, ISCIIA 2024, Proceedings",
address = "Germany",
}