بهبود رویکرد مدل‌سازی فازی Sugeno-Yasukawa با استفاده از روش خوشه‌بندی و تصویرسازی جدید برای تقسیم بندی فضای ورودی

نوع مقاله : علمی-پژوهشی

نویسنده

گروه کامپیوتر - دانشکده مهندسی برق و کامپیوتر

چکیده

الگوریتم های محاسباتی نرم مانند منطق فازی، شبکه های عصبی و الگوریتم های تکاملی به طور گسترده در بسیاری از زمینه ها استفاده می شوند. به ویژه منطق فازی، به دلیل توانایی قابل توجهی که در مدل سازی دارد، محبوبیت قابل توجهی به دست آورده است. تاکنون روش های مختلفی برای مدل سازی فازی ارائه شده است که هر یک از این روش ها مزایا و معایب خود را دارند. در حالی که اکثر روش ها تجزیه و تحلیل را از ورودی دارند ولی روش SY از بخش خروجی به تجزیه و تحلیل سیستم می پردازد. محبوبیت روش SY را می توان به الگوریتم استخراج قانون موثر آن نسبت داد، که از یک فرآیند خوشه بندی برای تعیین توابع عضویت ورودی استفاده می کند. در این مقاله، یک الگوریتم جستجوی خوشه‌ای و یک روش تقسیم بندی فازی جدید پیشنهاد شده است که نگاشت فضای خروجی به فضای ورودی را با توزیع توابع گاوسی برای هر نقطه داده در یک خوشه و محاسبه مقادیر عضویت آن‌ها افزایش می‌دهد. با این روش جدید جستجوی خوشه‌بندی پیشنهادی، عملکرد روش SY بهبود یافته است. با توجه به شبیه‌سازی ها، روش پیشنهادی معیار میانگین مربعات خطا (MSE) را 0.001 و معیار دقت را 1.5 درصد بهبود بخشیده است.

کلیدواژه‌ها

عنوان مقاله [English]

ISY: Improved Sugeno-Yasukawa Fuzzy Modelling Approach Using a Novel Clustering and Project Method for Input Partitioning

نویسنده [English]

  • S. Haghzad Klidbary
Department Electrical and Computer Engineering, University of Zanjan, Zanjan, Iran.
چکیده [English]

Soft computing algorithm such as fuzzy logic, neural networks, and evolutionary algorithms are widely used in many fields. Fuzzy logic, in particular, has gained significant popularity due to its significant ability in modelling. So far, various methods of fuzzy modelling have been presented; each of these methods has its advantages and disadvantages. While all methods start from the input, Sugeno-Yasukawa (SY) differs by initiating the analysis from the output. The popularity of the SY method can be attributed to its effective rule extraction algorithm, which employs a clustering process to determine input membership functions. In this paper, we propose a cluster search algorithm and a new fuzzy partitioning method that enhance the mapping of the output space to the input space by distributing Gaussian functions for each data point within a cluster and calculating their membership values. With this proposed new clustering search method, the performance of the SY method is improved. Through simulations, the proposed method has improved the mean square errors (MSE) criterion by 0.001, and improved the accuracy criterion by 1.5 percent.

کلیدواژه‌ها [English]

  • Fuzzy Inference System (FIS)
  • Fuzzy Modelling
  • Sugeno-Yasukawa (SY) Method
  • Membership Function Approximation
  • Clustering

مراجع

[1].  Deng, J., & Deng, Y. (2021). Information volume of fuzzy membership function. International Journal of Computers Communications & Control, 16(1).
[2].  Nabijonov, R. (2022). Theories of fuzzy sets and their application in face recognition. Innovation in the modern education system.
[3].  Akram, M., & Naz, S. (2019). A novel decision-making approach under complex Pythagorean fuzzy environment. Mathematical and Computational Applications, 24(3), 73.
[4].  Štěpnička, M., Holčapek, M., & Škorupová, N. (2019, June). Orderings of extensional fuzzy numbers. In 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-6). IEEE.
[5].  Tranquillo, J. V. (2019). An introduction to complex systems. Lewisburg: Springer International Publishing.
[6].  Mamdani, E.H. and S. Assilian, An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 1975. 7(1): p. 1-13.
[7].  Takagi, T. and M. Sugeno, Fuzzy identification of systems and its applications to modeling and control. IEEE transactions on systems, man, and cybernetics, 1985(1): p. 116-132.
[8].  Ying, H., Sufficient conditions on uniform approximation of multivariate functions by general Takagi-Sugeno fuzzy systems with linear rule consequent. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 1998. 28(4): p. 515-520.
[9].  Ying, H., Sufficient conditions on general fuzzy systems as function approximators. Automatica, 1994. 30(3): p. 521-525.
[10]. Tanaka, K. and H.O. Wang, Fuzzy control systems design and analysis: a linear matrix inequality approach. 2004: John Wiley & Sons.
[11]. Shouraki, S.B., A novel fuzzy approach to modeling and control and its hardware implementation based on brain functionality and specifications. 2000.
[12]. Sugeno, M. and T. Yasukawa, A fuzzy-logic-based approach to qualitative modeling. IEEE Transactions on fuzzy systems, 1993. 1(1): p. 7.
[13]. Camastra, F., et al., A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference. Expert Systems with Applications, 2015. 42(3): p. 1710-1716.
[14]. Khan, D.A. and S. Abbas, Intelligent Transportation System for Smart-Cities using Fuzzy Logic. Lahore Garrison Univ. Res. J. Comput. Sci. Inf. Technol, 2018. 2: p. 64-79.
[15]. Rustum, R., et al., Sustainability ranking of desalination plants using mamdani fuzzy logic inference systems. Sustainability, 2020. 12(2): p. 631.
[16]. Martinez-Gil, J. and J.M. Chaves-Gonzalez, Interpretable ontology meta-matching in the biomedical domain using Mamdani fuzzy inference. Expert Systems with Applications, 2022. 188: p. 116025.
[17].        Georg, S., H. Schulte, and H. Aschemann. Control-oriented modelling of wind turbines using a Takagi-Sugeno model structure. in 2012 IEEE International Conference on Fuzzy Systems. 2012. IEEE.
[18]. Salgado, C.M., et al., Takagi–Sugeno fuzzy modeling using mixed fuzzy clustering. IEEE Transactions on Fuzzy Systems, 2016. 25(6): p. 1417-1429.
[19].Elias, L.J., et al., Stability analysis of Takagi–Sugeno systems using a switched fuzzy Lyapunov function. Information Sciences, 2021. 543: p. 43-57.
[20]. Chaubey, S. and V. Puig, Autonomous Vehicle State Estimation and Mapping Using Takagi–Sugeno Modeling Approach. Sensors, 2022. 22(9): p. 3399.
[21]. Javadian, M., A. Hejazi, and S.H. Klidbary, Obtaining Fuzzy Membership Function of Clusters With the Memristor Hardware Implementation and On-Chip Learning. IEEE Transactions on Emerging Topics in Computational Intelligence, 2022. 6(4): p. 1008-1025.
[22]. Javadian, M., et al., Refining membership degrees obtained from fuzzy C-means by re-fuzzification. Iranian Journal of Fuzzy Systems, 2020. 17(4): p. 85-104.
[23].  Jokar, E., et al., Hardware-algorithm co-design of a compressed fuzzy active learning method. IEEE Transactions on Circuits and Systems I: Regular Papers, 2020. 67(12): p. 4932-4945.
[24]. Klidbary, S.H., S.B. Shouraki, and B. Linares-Barranco, Digital hardware realization of a novel adaptive ink drop spread operator and its application in modeling and classification and on-chip training. International Journal of Machine Learning and Cybernetics, 2019. 10: p. 2541-2561.
[25]. Klidbary, S.H., et al. Outlier robust fuzzy active learning method (ALM). in 2017 7th international conference on computer and knowledge engineering (ICCKE). 2017. IEEE.
[26]. Merrikh-Bayat, F., S.B. Shouraki, and A. Rohani, Memristor crossbar-based hardware implementation of the IDS method. IEEE Transactions on Fuzzy Systems, 2011. 19(6): p. 1083-1096.
[27]. Tikk, D., et al., Improvements and critique on Sugeno's and Yasukawa's qualitative modeling. IEEE Transactions on Fuzzy Systems, 2002. 10(5): p. 596-606.
[28]. Hadad, A.H., T.D. Gedeon, and B.S.U. Mendis. Finding input sub-spaces for polymorphic fuzzy signatures. in 2009 IEEE International Conference on Fuzzy Systems. 2009. IEEE.
[29]. Tikk, D., et al. Implementation details of problems in Sugeno and Yasukawa's qualitative modeling. in Research Working Paper RWP-IT-02-2001, School of Information Technology. 2001.
[30]. Wong, K.W., et al. Improvement of the cluster searching algorithm in Sugeno and Yasukawa’s qualitative modeling approach. in Computational Intelligence. Theory and Applications: International Conference, 7th Fuzzy Days Dortmund, Germany, October 1–3, 2001 Proceedings 7. 2001. Springer.
[31]. Hadad, A.H., et al., A modification of Sugeno-Yasukawa modeler to improve structure identification phase. ACSE J, 2006. 6(3): p. 33-40.
[32]. Hadad, A.H., et al. A modified version of Sugeno-Yasukawa modeler. in Advances in Computer Science and Engineering: 13th International CSI Computer Conference, CSICC 2008 Kish Island, Iran, March 9-11, 2008 Revised Selected Papers. 2009. Springer.
[33]. Hadad, A.H., B.S.U. Mendis, and T.D. Gedeon. Improvements in Sugeno-Yasukawa modelling algorithm. in International Conference on Fuzzy Systems. 2010. IEEE.
[34].  سجاد حق زاد کلیدبری "ارائه اپراتور جدید جایگزین پخش قطره جوهر در روش یادگیری فعال" مجله مهندسی برق دانشگاه تبریز. 49.3  1055-1066(2019).
[35].  کدخدا, اکبرزاده توتونچی, صباحی. (2021). طبقه‌بند همباشی ادراکی مبتنی بر منطق فازی توسعه یافته. مجله مهندسی برق دانشگاه تبریز, 50(4), 1773-1784.‎
[36]. Wang, Y., Z. Pan, and J. Dong, A new two-layer nearest neighbor selection method for kNN classifier. Knowledge-Based Systems, 2023. 235: p. 107604.

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