Parameter Learning for the Nonlinear System Described by a Class of Hammerstein Models-Reference-Cited by-同舟云学术

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Parameter Learning for the Nonlinear System Described by a Class of Hammerstein Models

Published:2022-11-30 Issue:5 Volume:42 Page:2635-2653
ISSN:0278-081X
Container-title:Circuits, Systems, and Signal Processing
language:en
Short-container-title:Circuits Syst Signal Process

Author:

Li Feng^ORCID,Zhu Xinjian,Cao Qingfeng

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Changzhou Sci&Tech Program

Zhongwu Youth Innovative Talents Support Program in Jiangsu University of Technology

Blue Project of Universities in Jiangsu Province

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s00034-022-02240-y.pdf

Reference41 articles.

1. L. Aggoune, Y. Chetouani, Modeling of a distillation column based on NARMAX and Hammerstein models. Int. J. Model. Simul. Sci. Comput. 8(3), 1–14 (2017)

2. M. Al-Dhaifallah, K. Nisar, P. Agarwal et al., Modeling and identification of heat exchanger process using least squares support vector machines. Therm. Sci. 21(6B), 2859–2869 (2017)

3. A.R. Belabad, S. Sharifian, S.A. Motamedi, An accurate digital baseband predistorter design for linearization of RF power amplifiers by a genetic algorithm based Hammerstein structure. Analog Integr. Circ. Sig. Process. 95(2), 231–247 (2018)

4. A.W. Bowman, A. Azzalini, Applied Smoothing Techniques for Data Analysis (Oxford University Press, 1997)

5. J. Chen, X. Wang, Identification of Hammerstein systems with continuous nonlinearity. Inf. Process. Lett. 115(11), 822–827 (2015)

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