Design of fractional hierarchical gradient descent algorithm for parameter estimation of nonlinear control autoregressive systems
The trend of developing fractional gradient based iterative adaptive strategies is evolved in the recent years through effectively exploring the fractional and fractal dynamics. In this study, fractional hierarchical gradient descent (FHGD) is proposed by generalizing the standard hierarchical gradi...
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| Vydáno v: | Chaos, solitons and fractals Ročník 157; s. 111913 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
01.04.2022
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| Témata: | |
| ISSN: | 0960-0779, 1873-2887 |
| On-line přístup: | Získat plný text |
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| Abstract | The trend of developing fractional gradient based iterative adaptive strategies is evolved in the recent years through effectively exploring the fractional and fractal dynamics. In this study, fractional hierarchical gradient descent (FHGD) is proposed by generalizing the standard hierarchical gradient descent (HGD) to fractional order for effectively solving nonlinear system identification problem. The FHGD is effectively to applied to estimate the parameters of nonlinear control autoregressive (NCAR) systems under different fractional order and noise conditions. The fractional order greater than 1 provides faster convergence speed, less than 1 gives better steady state performance and equal to 1 reduces the FHGD to HGD. The accurate estimation of NCAR system parameters representing electrically stimulated muscle model validates the efficacy and robustness of the proposed FHGD in comparison with the standard HGD. |
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| AbstractList | The trend of developing fractional gradient based iterative adaptive strategies is evolved in the recent years through effectively exploring the fractional and fractal dynamics. In this study, fractional hierarchical gradient descent (FHGD) is proposed by generalizing the standard hierarchical gradient descent (HGD) to fractional order for effectively solving nonlinear system identification problem. The FHGD is effectively to applied to estimate the parameters of nonlinear control autoregressive (NCAR) systems under different fractional order and noise conditions. The fractional order greater than 1 provides faster convergence speed, less than 1 gives better steady state performance and equal to 1 reduces the FHGD to HGD. The accurate estimation of NCAR system parameters representing electrically stimulated muscle model validates the efficacy and robustness of the proposed FHGD in comparison with the standard HGD. |
| ArticleNumber | 111913 |
| Author | Khan, Zeshan Aslam Shah, Syed Muslim Mehmood, Ammara Chaudhary, Naveed Ishtiaq Raja, Muhammad Asif Zahoor |
| Author_xml | – sequence: 1 givenname: Naveed Ishtiaq surname: Chaudhary fullname: Chaudhary, Naveed Ishtiaq organization: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan – sequence: 2 givenname: Muhammad Asif Zahoor orcidid: 0000-0001-9953-822X surname: Raja fullname: Raja, Muhammad Asif Zahoor email: rajamaz@yuntech.edu.tw organization: Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan – sequence: 3 givenname: Zeshan Aslam surname: Khan fullname: Khan, Zeshan Aslam email: zeeshan.aslam@iiu.edu.pk organization: Department of Electrical Engineering, International Islamic University, Islamabad, Pakistan – sequence: 4 givenname: Ammara surname: Mehmood fullname: Mehmood, Ammara email: ammara.mehmood@adelaide.edu.au organization: School of Electrical and Electronic Engineering, The University of Adelaide, Australia – sequence: 5 givenname: Syed Muslim surname: Shah fullname: Shah, Syed Muslim email: s.m.shah@ieee.org organization: Department of Electrical Engineering, Capital University of Science and Technology, Islamabad, Pakistan |
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