Dynamic opposite learning enhanced artificial ecosystem optimizer for IIR system identification-Reference-Cited by-同舟云学术

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Dynamic opposite learning enhanced artificial ecosystem optimizer for IIR system identification

Published:2022-03-14 Issue:11 Volume:78 Page:13040-13085
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Niu Yanbiao,Yan Xuefeng^ORCID,Wang Yongzhen,Niu Yanzhao

Funder

the National Key R&D Program of China, Key Technology Research and Platform Development for Cloud Manufacturing Based on Open Architecture

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-022-04367-w.pdf

Reference45 articles.

1. Zarai K, Cherif A (2021) Adaptive filter based on Monte Carlo method to improve the non-linear target tracking in the radar system. Aerosp Syst 4:67–74. https://doi.org/10.1007/s42401-020-00080-9

2. Li W, Xiang Z, Ren P, Li Q (2021) Adaptive filter bank multi-carrier waveform design for joint communication-radar system. Digit Sign Proc 110:102950. https://doi.org/10.1016/j.dsp.2020.102950

3. Milosavljevic MS, Corron NJ, Blakely JN (2020) Optimal communications with infinite impulse response matched filters. Chaos, Solitons Fractals 138:109822. https://doi.org/10.1016/j.chaos.2020.109822

4. Le Cong D, Zhang J, Pang Y (2021) A novel pipelined neural FIR architecture for nonlinear adaptive filter. Neurocomputing 440:220–229. https://doi.org/10.1016/j.neucom.2020.11.036

5. Samalla DK, SubbaRao SPV, Mallikarjuna Rao G, Jagadeesh BN (2021) Design and development of adaptive filter for real time signal processing. IOP Conf Ser: Mater Sci Eng 1074:012036. https://doi.org/10.1088/1757-899X/1074/1/012036

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