Identification of Hammerstein Systems with Random Fourier Features and Kernel Risk Sensitive Loss
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Computer Networks and Communications,General Neuroscience,Software
Link
https://link.springer.com/content/pdf/10.1007/s11063-023-11191-7.pdf
Reference63 articles.
1. Pawlak M, Lv J (2022) Nonparametric testing for Hammerstein systems. IEEE Trans Autom Control. https://doi.org/10.1109/TAC.2022.3171389
2. Rahati Belabad A, Sharifian S, Motamedi SA (2018) An accurate digital baseband predistorter design for linearization of RF power amplifiers by a genetic algorithm based Hammerstein structure. Analog Integr Circuits Process 95(2):231–247
3. Jurado F (2006) A method for the identification of solid oxide fuel cells using a Hammerstein model. J Power Sources 154(1):145–152
4. Capobianco E (2002) Hammerstein system represention of financial volatility processes. Eur Phys J B-Condens Matter Complex Syst 27(2):201–211
5. Liu Z, Li C (2022) Adaptive Hammerstein filtering via recursive non-convex projection. IEEE Trans Signal Process. https://doi.org/10.1109/TSP.2022.3180195
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