Optimizing alpha–beta filter for enhanced predictions accuracy in industrial applications using Mamdani fuzzy inference system
This work presents a novel approach for dynamically optimizing the alpha–beta filter parameters through the Mamdani fuzzy inference system (MFIS) for industrial applications to estimate the state of dynamic systems based on sensor measurements. Our proposed method has two important components: the p...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825001437 |
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| author | Junaid Khan Muhammad Fayaz Umar Zaman Eunkyu Lee Awatef Salim Balobaid Muhammad Bilal Kyungsup Kim |
| author_facet | Junaid Khan Muhammad Fayaz Umar Zaman Eunkyu Lee Awatef Salim Balobaid Muhammad Bilal Kyungsup Kim |
| author_sort | Junaid Khan |
| collection | DOAJ |
| description | This work presents a novel approach for dynamically optimizing the alpha–beta filter parameters through the Mamdani fuzzy inference system (MFIS) for industrial applications to estimate the state of dynamic systems based on sensor measurements. Our proposed method has two important components: the primary predictor utilizing the alpha–beta algorithm, and a rule-based mechanism leveraging the Mamdani fuzzy inference system. To illustrate our approach and simplify the demonstration, we selected two types of sensors: temperature and humidity. The model efficiently processes input from these sensors, refining the sensor data to filter out noise and improve prediction accuracy. The integration of MFIS significantly improves the system’s performance, significantly reducing the root mean square error (RMSE) and mean absolute error (MAE), which are critical indicators of predictive accuracy. To validate the effectiveness and robustness of our method, we executed an extensive set of experiments , which affirm the superior performance of our model. |
| format | Article |
| id | doaj-art-688fca0896d449f5a93d2e1e29d65fa4 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-688fca0896d449f5a93d2e1e29d65fa42025-02-11T04:33:37ZengElsevierAlexandria Engineering Journal1110-01682025-04-01119598608Optimizing alpha–beta filter for enhanced predictions accuracy in industrial applications using Mamdani fuzzy inference systemJunaid Khan0Muhammad Fayaz1Umar Zaman2Eunkyu Lee3Awatef Salim Balobaid4Muhammad Bilal5Kyungsup Kim6Department of Environmental IT Engineering, Chungnam National University, Daejeon, 34134, South Korea; Autonomous Ship Research Center, Samsung Heavy Industries, Daejeon, 34051, South Korea; Corresponding authors: Kyungsup Kim (sclkim@cnu.ac.kr) and Junaid Khan (junaid1.khan@samsung.com)Department of Computer Science, University of Central Asia, Naryn, KyrgyzstanDepartment of Artificial Intelligence, Chungnam National University, Daejeon, 34134, South KoreaAutonomous Ship Research Center, Samsung Heavy Industries, Daejeon, 34051, South KoreaDepartment of Computer Science, College of Computer Science and Information Technology, Jazan University, Jazan 45142, Saudi ArabiaSchool of Computing and Communications, Lancaster University, Lancaster, LA1 4WA, United KingdomDepartment of Computer Engineering, Chungnam National University, Daejeon, 34134, South Korea; Corresponding authors: Kyungsup Kim (sclkim@cnu.ac.kr) and Junaid Khan (junaid1.khan@samsung.com)This work presents a novel approach for dynamically optimizing the alpha–beta filter parameters through the Mamdani fuzzy inference system (MFIS) for industrial applications to estimate the state of dynamic systems based on sensor measurements. Our proposed method has two important components: the primary predictor utilizing the alpha–beta algorithm, and a rule-based mechanism leveraging the Mamdani fuzzy inference system. To illustrate our approach and simplify the demonstration, we selected two types of sensors: temperature and humidity. The model efficiently processes input from these sensors, refining the sensor data to filter out noise and improve prediction accuracy. The integration of MFIS significantly improves the system’s performance, significantly reducing the root mean square error (RMSE) and mean absolute error (MAE), which are critical indicators of predictive accuracy. To validate the effectiveness and robustness of our method, we executed an extensive set of experiments , which affirm the superior performance of our model.http://www.sciencedirect.com/science/article/pii/S1110016825001437Alpha–beta filterAlgorithmsDynamic systemMamdani fuzzy inference systemRule based systemIndustrial applications |
| spellingShingle | Junaid Khan Muhammad Fayaz Umar Zaman Eunkyu Lee Awatef Salim Balobaid Muhammad Bilal Kyungsup Kim Optimizing alpha–beta filter for enhanced predictions accuracy in industrial applications using Mamdani fuzzy inference system Alexandria Engineering Journal Alpha–beta filter Algorithms Dynamic system Mamdani fuzzy inference system Rule based system Industrial applications |
| title | Optimizing alpha–beta filter for enhanced predictions accuracy in industrial applications using Mamdani fuzzy inference system |
| title_full | Optimizing alpha–beta filter for enhanced predictions accuracy in industrial applications using Mamdani fuzzy inference system |
| title_fullStr | Optimizing alpha–beta filter for enhanced predictions accuracy in industrial applications using Mamdani fuzzy inference system |
| title_full_unstemmed | Optimizing alpha–beta filter for enhanced predictions accuracy in industrial applications using Mamdani fuzzy inference system |
| title_short | Optimizing alpha–beta filter for enhanced predictions accuracy in industrial applications using Mamdani fuzzy inference system |
| title_sort | optimizing alpha beta filter for enhanced predictions accuracy in industrial applications using mamdani fuzzy inference system |
| topic | Alpha–beta filter Algorithms Dynamic system Mamdani fuzzy inference system Rule based system Industrial applications |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825001437 |
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