A Simple Tuning Algorithm of Augmented Fuzzy Membership Functions
The successful application of fuzzy control depends to a large extent on the parameters of some subjective decisions, such as fuzzy membership function (MF). Fuzzy logic controller (FLC) implementing augmented output MFs as compare to input MFs is presented to improve the accuracy, robustness, and p...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9000856/ |
| _version_ | 1819295715436789760 |
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| author | Salah-Ud-Din Khokhar Qinke Peng Ali Asif Muhammad Yasir Noor Aaqib Inam |
| author_facet | Salah-Ud-Din Khokhar Qinke Peng Ali Asif Muhammad Yasir Noor Aaqib Inam |
| author_sort | Salah-Ud-Din Khokhar |
| collection | DOAJ |
| description | The successful application of fuzzy control depends to a large extent on the parameters of some subjective decisions, such as fuzzy membership function (MF). Fuzzy logic controller (FLC) implementing augmented output MFs as compare to input MFs is presented to improve the accuracy, robustness, and performance of the system. The best possible combination of input and output MFs is introduced to distribute the uniform input MFs and augmented output MFs in the treatise. The simulation of the 2-Inputs 1-Output Fuzzy Control System is performed in many nonlinear processes. Then, the experimental outcomes of the uniformly and augmented distributed output MFs are compared under similar circumstances. The experimental outcomes are in a virtuous covenant with the simulation outcomes. The experimental outcomes show that the root mean square error (RMSE) is reduced around 75.3% and bringing down the relative error to the acceptable range (≤±10%). The control accuracy is improved and the robustness is boosted by reducing the RMSE through the FLC with augmented-distributed output MFs. Moreover, the cost and energy efficiency in any fuzzy system will be improved by implementing the augmented-distributed output MFs using the best possible combination of input and output MFs. |
| first_indexed | 2024-12-24T04:46:37Z |
| format | Article |
| id | doaj.art-2646cd6604ac4d0388d48dcc54af3aea |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T04:46:37Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2646cd6604ac4d0388d48dcc54af3aea2022-12-21T17:14:40ZengIEEEIEEE Access2169-35362020-01-018358053581410.1109/ACCESS.2020.29745339000856A Simple Tuning Algorithm of Augmented Fuzzy Membership FunctionsSalah-Ud-Din Khokhar0https://orcid.org/0000-0002-5016-4391Qinke Peng1https://orcid.org/0000-0002-5448-8529Ali Asif2Muhammad Yasir Noor3Aaqib Inam4Department of Automation Science and Technology (System Engineering Institute), School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Automation Science and Technology (System Engineering Institute), School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, ChinaDepartment of Physics, GC University Lahore, Lahore, PakistanDepartment of Physics, GC University Lahore, Lahore, PakistanSchool of Software Engineering, Xi’an Jiaotong University, Xi’an, ChinaThe successful application of fuzzy control depends to a large extent on the parameters of some subjective decisions, such as fuzzy membership function (MF). Fuzzy logic controller (FLC) implementing augmented output MFs as compare to input MFs is presented to improve the accuracy, robustness, and performance of the system. The best possible combination of input and output MFs is introduced to distribute the uniform input MFs and augmented output MFs in the treatise. The simulation of the 2-Inputs 1-Output Fuzzy Control System is performed in many nonlinear processes. Then, the experimental outcomes of the uniformly and augmented distributed output MFs are compared under similar circumstances. The experimental outcomes are in a virtuous covenant with the simulation outcomes. The experimental outcomes show that the root mean square error (RMSE) is reduced around 75.3% and bringing down the relative error to the acceptable range (≤±10%). The control accuracy is improved and the robustness is boosted by reducing the RMSE through the FLC with augmented-distributed output MFs. Moreover, the cost and energy efficiency in any fuzzy system will be improved by implementing the augmented-distributed output MFs using the best possible combination of input and output MFs.https://ieeexplore.ieee.org/document/9000856/Fuzzy control systemmembership functionsrelative errorroot mean square error |
| spellingShingle | Salah-Ud-Din Khokhar Qinke Peng Ali Asif Muhammad Yasir Noor Aaqib Inam A Simple Tuning Algorithm of Augmented Fuzzy Membership Functions IEEE Access Fuzzy control system membership functions relative error root mean square error |
| title | A Simple Tuning Algorithm of Augmented Fuzzy Membership Functions |
| title_full | A Simple Tuning Algorithm of Augmented Fuzzy Membership Functions |
| title_fullStr | A Simple Tuning Algorithm of Augmented Fuzzy Membership Functions |
| title_full_unstemmed | A Simple Tuning Algorithm of Augmented Fuzzy Membership Functions |
| title_short | A Simple Tuning Algorithm of Augmented Fuzzy Membership Functions |
| title_sort | simple tuning algorithm of augmented fuzzy membership functions |
| topic | Fuzzy control system membership functions relative error root mean square error |
| url | https://ieeexplore.ieee.org/document/9000856/ |
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