Identifying of Volterra Frequency-Domain Kernels Based on Neural Network

WU Shihao; MENG Yafeng; WANG Chao

doi:10.3969/j.issn.1671-637x.2019.02.008

Journals >Electronics Optics & Control >Volume 26 >Issue 2 >Page 38 > Article

Electronics Optics & Control
Vol. 26, Issue 2, 38 (2019)

Identifying of Volterra Frequency-Domain Kernels Based on Neural Network

WU Shihao^1、2, MENG Yafeng¹, and WANG Chao³

Author Affiliations

¹[in Chinese]

²[in Chinese]

³[in Chinese]

show less

DOI: 10.3969/j.issn.1671-637x.2019.02.008 Cite this Article

WU Shihao, MENG Yafeng, WANG Chao. Identifying of Volterra Frequency-Domain Kernels Based on Neural Network[J]. Electronics Optics & Control, 2019, 26(2): 38 Copy Citation Text

show less

References

[1] ZHU A, BRAIL T J. Behavioral modeling of RF power amplifiers based on pruned Volterra series［J］.IEEE Microwave and Wireless Components Letters, 2004, 14:563-565.

[2] KERSCHEN G, WORDEN K, VAKAKIS A F, et al. Past,present and future of nonlinear system identification in structural dynamics［J］.Mechanicial Systems and Signal Processing, 2006, 20(3):505-592.

[4] HAN C Z. A general formula of generalized frequency response functions of nonlinear differential equations［R］. Xi'an: Xi'an Jiaotong University, 1992.

[8] HAN H T, MA H G, TAN L N, et al. Non-parametric identification method of Volterra kernels for nonlinear systems excited by multitone signal［J］. Asian Journal of Control, 2014, 16(2): 519-529.

CLP Journals

[1] CHENG Baozhi, ZHAO Chunhui, ZHANG Lili. Research Advances of Anomaly Target Detection Algorithms for Hyperspectral Imagery[J]. Electronics Optics & Control, 2021, 28(5): 56

WU Shihao, MENG Yafeng, WANG Chao. Identifying of Volterra Frequency-Domain Kernels Based on Neural Network[J]. Electronics Optics & Control, 2019, 26(2): 38

Download Citation

Tools

Save the article for my favorites

Paper Information