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Bandwidth Selection for Kernel Generalized Regression Neural Networks in Identification of Hammerstein Systems Cover

Bandwidth Selection for Kernel Generalized Regression Neural Networks in Identification of Hammerstein Systems

Journal of Artificial Intelligence and Soft Computing Research
Volume 11 (2021): Issue 3 (July 2021)
By: Jiaqing Lv and  Mirosław Pawlak  
Open Access
|May 2021

Authors

Jiaqing Lv

Department of Electrical & Computer Engineering, University of Manitoba, Canada

Mirosław Pawlak

miroslaw.pawlak@umanitoba.ca

Department of Electrical & Computer Engineering, University of Manitoba, Canada
Information Technology Institute, University of Social Sciences, Lodz, Poland
DOI: https://doi.org/10.2478/jaiscr-2021-0011
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Language: English
Page range: 181 - 194
Submitted on: Aug 5, 2020
|
Accepted on: Jan 19, 2021
|
Published on: May 29, 2021
Published by: SAN University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Generalized regression neural networks,
nonparametric estimation,
bandwidth,
data-driven selection,
nonlinear systems,
Hammerstein systems
Related subjects:
Computer sciences,
Databases and data mining,
Artificial intelligence

© 2021 Jiaqing Lv, Mirosław Pawlak, published by SAN University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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