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Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

Salah-ud-din Khokhar1,2,*, QinKe Peng1, Muhammad Yasir Noor3

1 Department of Automation Science and Technology (System Engineering Institute), School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, Shaanxi, 710049, China
2 Department of Computer Science, G.C. University, Lahore, 54000, Punjab, Pakistan
3 Department of Physics (Electronics), G.C. University, Lahore, 54000, Punjab, Pakistan

* Corresponding Author: Salah-ud-din Khokhar. Email: email

Computers, Materials & Continua 2023, 75(2), 2413-2427. https://doi.org/10.32604/cmc.2023.036148

Abstract

For fuzzy systems to be implemented effectively, the fuzzy membership function (MF) is essential. A fuzzy system (FS) that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output (SISO) FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output. Utilizing a variety of non-linear techniques, a SISO FS is simulated. The results of FS experiments conducted in comparable conditions are then compared. The simulated results and the results of the experimental setup agree fairly well. The findings of the suggested model demonstrate that the relative error is abated to a sufficient range (≤ ± 10%) and that the mean absolute percentage error (MPAE) is reduced by around 66.2%. The proposed strategy to reduce MAPE using an FS improves the system’s performance and control accuracy. By using the best input and output MFs protocol, the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs. The proposed fuzzy system performed far better than other modern days approaches available in the literature.

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Cite This Article

S. Khokhar, Q. Peng and M. Y. Noor, "Output linearization of single-input single-output fuzzy system to improve accuracy and performance," Computers, Materials & Continua, vol. 75, no.2, pp. 2413–2427, 2023.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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