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ROBUST FUZZY MODELS FOR ULTRASONIC POLYMER DEGRADATION

Year 2025, Volume: 10 Issue: 1, 40 - 51, 27.03.2025
https://doi.org/10.57120/yalvac.1644658

Abstract

Fuzzy Logic Models are practical solutions to reach a definite conclusion in data sets with uncertain, complicated, and incomplete input data. Owing to these models, achieving the desired outputs with very low error in large data sets obtained theoretically or experimentally is possible. In this study, a subtractive clustering based fuzzy model approach has been presented to analyze the ultrasonic polymer degradation. Fuzzy models include obtaining cluster centers from the data set, preparing a fuzzy rule-based linear equation system, and optimizing parameters for the least error. The designed fuzzy models have high accuracy and clearly express ultrasonic degradation behavior

References

  • 1. Mason T.J. (1996). Sonochemistry: Uses of Ultrasound in Chemistry and Related Disciplines, Ultrasound Angioplasty, 178, 25-54.
  • 2. Villamiel, M., Cortés-Avendaño, P., Ferreira-Lazarte, A., & Condezo-Hoyos, L. (2025). Chemistry of ultrasound processing. In Chemistry of Thermal and Non-Thermal Food Processing Technologies, Academic Press, 175-199.
  • 3. Akyüz, A., Giz, A., & Catalgil-Giz, H. (2018). Ultrasonic Chain Scission of Polyacrylamide in Solution: Online Monitoring Results and Comparison with Theoretical Models. Journal of Macromolecular Science, Part B, 57(7), 527-540.
  • 4. Siddique, M., Rashid, R., & Ali, A. (2025). Fundamentals of acoustic cavitation, ultrasound-assisted processes, and sonochemistry. In Modeling and Simulation of Sono-Processes, Elsevier, 3-17. https://doi.org/10.1016/B978-0-443-23651-8.00001-2
  • 5. Akyüz, A., Kamer, O., & Giz, A. (2013). Online viscometric monitoring of ultrasonic sodium poly (styrene sulfonate) scission. Journal of Macromolecular Science, Part A, 50(5), 535-540.
  • 6. Kerboua, K. (2025). Sonochemistry and acoustic cavitation bubble: modeling and simulation. In Modeling and Simulation of Sono-Processes, Elsevier, 185-200. https://doi.org/10.1016/B978-0-443-23651-8.00012-7
  • 7. Gungor, A., Akyuz, A. O., Şirin, C., Tuncer, A. D., Zaman, M., & Gungor, C. (2019). Importance of mathematical modeling in innovation. Mathematical Modeling, 3(1), 32-34.
  • 8. Akyüz, A. (2024). Effect of Temperature on Ultrasonic Degradation of Sodium Poly (Styrene Sulfonate): Analysis of Online Viscometric Data with Theoretical Models and Machine Learning Approaches. Journal of Macromolecular Science, Part B, 63(12), 1379-1403.
  • 9. Bezdek, J.C., Ehrlich, R., Full, W. (1984) FCM: The Fuzzy c-means Clustering Algorithm. Computers and Geosciences. 10 (2-3), 191–203.
  • 10. Demirli K., Cheng S.X. and Muthukumaran P. (2003) Subtractive clustering based on modelling of job sequencing with parametric search. Fuzzy Sets and Systems. 137 (2), 235–270.
  • 11. Setnes, M., Babuska, R., Verbruggen, H.B. (1998) Transparent Fuzzy Modelling. International Journal of Human-Computer Studies. 49 (2), 159–179.
  • 12. Takagi T., Sugeno M. (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15 (1), 116–132.
  • 13. Miraftab V., Mansour R.R. (2006) EM-based microwave circuit design using fuzzy logic techniques. IEE Proceedings - Microwaves Antennas and Propagation, 153 (6), 495–501.
  • 14. Mamdani E.H. (1974) Applications of Fuzzy Algorithms for Simple Dynamic Plant. IEE Proceedings – Control & Science, 121 (12), 1585–1588.
  • 15. Piegat A. (2001) Fuzzy Modeling and Control. Springer Science & Business Media.
  • 16. Sugeno, M., Tanaka, K. (1991) Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets and Systems, 42 (3), 315–334.
  • 17. Miraftab V., Mansour R.R. (2004) A Robust Fuzzy-Logic Technique for Computer-Aided Diagnosis of Microwave Filters. IEEE Transactions on Microwave Theory and Techniques, 52 (1), 450–456.
  • 18. Yager R.R., Filev D.P. (1994) Approximate Clustering Via the Mountain Method. IEEE Transactions on Systems, Man, and Cybernetics, 24(8), 1279-1284.
  • 19. Chiu S.L. (1994) Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2(3), 267-278.
  • 20. Sugeno M., Tanaka K. (1991) Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets and Systems, 42(3), 315-334.
  • 21. Akyüz, A., Giz, A., & Catalgil-Giz, H. (2018). Ultrasonic Chain Scission of Polyacrylamide in Solution: Online Monitoring Results and Comparison with Theoretical Models. Journal of Macromolecular Science, Part B, 57(7), 527-540.
  • 22. Akyüz, A. (2017). Polietilen Oksitin Ultrasonik Zincir Kırılması: Konsantrasyon ve Sıcaklık Etkisi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 17(1), 109-116.

ROBUST FUZZY MODELS FOR ULTRASONIC POLYMER DEGRADATION

Year 2025, Volume: 10 Issue: 1, 40 - 51, 27.03.2025
https://doi.org/10.57120/yalvac.1644658

Abstract

Fuzzy Logic Models are practical solutions to reach a definite conclusion in data sets with uncertain, complicated, and incomplete input data. Owing to these models, achieving the desired outputs with very low error in large data sets obtained theoretically or experimentally is possible. In this study, a subtractive clustering based fuzzy model approach has been presented to analyze the ultrasonic polymer degradation. Fuzzy models include obtaining cluster centers from the data set, preparing a fuzzy rule-based linear equation system, and optimizing parameters for the least error. The designed fuzzy models have high accuracy and clearly express ultrasonic degradation behavior.

References

  • 1. Mason T.J. (1996). Sonochemistry: Uses of Ultrasound in Chemistry and Related Disciplines, Ultrasound Angioplasty, 178, 25-54.
  • 2. Villamiel, M., Cortés-Avendaño, P., Ferreira-Lazarte, A., & Condezo-Hoyos, L. (2025). Chemistry of ultrasound processing. In Chemistry of Thermal and Non-Thermal Food Processing Technologies, Academic Press, 175-199.
  • 3. Akyüz, A., Giz, A., & Catalgil-Giz, H. (2018). Ultrasonic Chain Scission of Polyacrylamide in Solution: Online Monitoring Results and Comparison with Theoretical Models. Journal of Macromolecular Science, Part B, 57(7), 527-540.
  • 4. Siddique, M., Rashid, R., & Ali, A. (2025). Fundamentals of acoustic cavitation, ultrasound-assisted processes, and sonochemistry. In Modeling and Simulation of Sono-Processes, Elsevier, 3-17. https://doi.org/10.1016/B978-0-443-23651-8.00001-2
  • 5. Akyüz, A., Kamer, O., & Giz, A. (2013). Online viscometric monitoring of ultrasonic sodium poly (styrene sulfonate) scission. Journal of Macromolecular Science, Part A, 50(5), 535-540.
  • 6. Kerboua, K. (2025). Sonochemistry and acoustic cavitation bubble: modeling and simulation. In Modeling and Simulation of Sono-Processes, Elsevier, 185-200. https://doi.org/10.1016/B978-0-443-23651-8.00012-7
  • 7. Gungor, A., Akyuz, A. O., Şirin, C., Tuncer, A. D., Zaman, M., & Gungor, C. (2019). Importance of mathematical modeling in innovation. Mathematical Modeling, 3(1), 32-34.
  • 8. Akyüz, A. (2024). Effect of Temperature on Ultrasonic Degradation of Sodium Poly (Styrene Sulfonate): Analysis of Online Viscometric Data with Theoretical Models and Machine Learning Approaches. Journal of Macromolecular Science, Part B, 63(12), 1379-1403.
  • 9. Bezdek, J.C., Ehrlich, R., Full, W. (1984) FCM: The Fuzzy c-means Clustering Algorithm. Computers and Geosciences. 10 (2-3), 191–203.
  • 10. Demirli K., Cheng S.X. and Muthukumaran P. (2003) Subtractive clustering based on modelling of job sequencing with parametric search. Fuzzy Sets and Systems. 137 (2), 235–270.
  • 11. Setnes, M., Babuska, R., Verbruggen, H.B. (1998) Transparent Fuzzy Modelling. International Journal of Human-Computer Studies. 49 (2), 159–179.
  • 12. Takagi T., Sugeno M. (1985) Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15 (1), 116–132.
  • 13. Miraftab V., Mansour R.R. (2006) EM-based microwave circuit design using fuzzy logic techniques. IEE Proceedings - Microwaves Antennas and Propagation, 153 (6), 495–501.
  • 14. Mamdani E.H. (1974) Applications of Fuzzy Algorithms for Simple Dynamic Plant. IEE Proceedings – Control & Science, 121 (12), 1585–1588.
  • 15. Piegat A. (2001) Fuzzy Modeling and Control. Springer Science & Business Media.
  • 16. Sugeno, M., Tanaka, K. (1991) Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets and Systems, 42 (3), 315–334.
  • 17. Miraftab V., Mansour R.R. (2004) A Robust Fuzzy-Logic Technique for Computer-Aided Diagnosis of Microwave Filters. IEEE Transactions on Microwave Theory and Techniques, 52 (1), 450–456.
  • 18. Yager R.R., Filev D.P. (1994) Approximate Clustering Via the Mountain Method. IEEE Transactions on Systems, Man, and Cybernetics, 24(8), 1279-1284.
  • 19. Chiu S.L. (1994) Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems, 2(3), 267-278.
  • 20. Sugeno M., Tanaka K. (1991) Successive identification of a fuzzy model and its applications to prediction of a complex system. Fuzzy Sets and Systems, 42(3), 315-334.
  • 21. Akyüz, A., Giz, A., & Catalgil-Giz, H. (2018). Ultrasonic Chain Scission of Polyacrylamide in Solution: Online Monitoring Results and Comparison with Theoretical Models. Journal of Macromolecular Science, Part B, 57(7), 527-540.
  • 22. Akyüz, A. (2017). Polietilen Oksitin Ultrasonik Zincir Kırılması: Konsantrasyon ve Sıcaklık Etkisi. Afyon Kocatepe Üniversitesi Fen Ve Mühendislik Bilimleri Dergisi, 17(1), 109-116.
There are 22 citations in total.

Details

Primary Language English
Subjects Electrical Engineering (Other)
Journal Section Articels
Authors

Onur İnan 0000-0002-9683-344X

Ali Özhan Akyüz 0000-0001-9265-7293

Early Pub Date March 24, 2025
Publication Date March 27, 2025
Submission Date February 21, 2025
Acceptance Date March 4, 2025
Published in Issue Year 2025 Volume: 10 Issue: 1

Cite

APA İnan, O., & Akyüz, A. Ö. (2025). ROBUST FUZZY MODELS FOR ULTRASONIC POLYMER DEGRADATION. Yalvaç Akademi Dergisi, 10(1), 40-51. https://doi.org/10.57120/yalvac.1644658

http://www.yalvacakademi.org/