Identification of fractional order Wiener-Hammerstein systems based on adaptively fuzzy PSO and data filtering technique-Reference-Cited by-同舟云学术

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Identification of fractional order Wiener-Hammerstein systems based on adaptively fuzzy PSO and data filtering technique

Published:2022-10-21 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Zong Tiancheng,Li Junhong,Lu Guoping

Funder

National Natural Science Foundation of China

Six Talent Peaks Project in Jiangsu Province

Postgraduate Research & Practice Innovation Program of Jiangsu Province

Natural Science Research Program of Jiangsu Colleges and Universities

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-04220-w.pdf

Reference30 articles.

1. Guo W, Xu P, Dai F, Hou Z (2022) Harris hawks optimization algorithm based on elite fractional mutation for data clustering. Applied Intelligence. https://doi.org/10.1007/s10489-021-02985-0https://doi.org/10.1007/s10489-021-02985-0

2. George T, Ganesan V (2022) Optimal tuning of FOPID controller for higher order process using hybrid approach. Applied Intelligence. https://doi.org/10.1007/s10489-022-03167-2

3. Xia ZQ, Wang XY, Wang CP, Wang CX, Ma B, Li Q, Wang MX, Zhao TT (2022) A robust zero-watermarking algorithm for lossless copyright protection of medical images. Appl Intell 52:607–621

4. Zhang Q, Wang HW, Liu CL (2021) Identification of fractional-order Hammerstein nonlinear ARMAX system with colored noise. Nonlinear Dyn 106(4):3215–3230

5. Galvão RKH, Teixeira MCM, Assunção E, Paiva HM, Hadjiloucas S (2020) Identification of fractional-order transfer functions using exponentially modulated signals with arbitrary excitation waveforms. ISA Trans 103:10–18

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