MODELING AND SIMULATION-BASED COMPARISON OF MAMDANI AND TAKAGI–SUGENO FUZZY INFERENCE APPROACHES
DOI:
https://doi.org/10.29121/ijetmr.v9.i11.2022.1764Keywords:
FIS, MFIS, TDFM, Virtualization, Defuzzification etcAbstract
In many different fields, fuzzy inference systems (FIS) have become effective tools for managing complex and uncertain systems. The Takagi-Sugeno Fuzzy Model (TSFM) and the Mamdani Fuzzy Inference System (MFIS) are the two most used FIS models. The fuzzy inference processes used in these two models are compared in this research. The Mamdani Fuzzy Inference System uses membership functions and linguistic variables in conjunction with fuzzy rules to map input variables to output variables. To produce clear output values, it uses a fuzzy rule basis, fuzzy logic operators, and defuzzification approaches. The MFIS's capacity to capture linguistic information through rule-based modeling makes it especially appropriate for handling complicated and nonlinear systems. The Takagi-Sugeno Fuzzy Model, on the other hand, is a fuzzy rule-based model that uses a collection of linear or nonlinear functions to mimic the behavior of a system. The TSFM directly uses input variables to create rule consequents rather than using language variables. This model is renowned for its computing efficiency, interpretability, and simplicity. The operating principles, architecture, and salient features of the Takagi-Sugeno Fuzzy Model and the Mamdani Fuzzy Inference System are examined and contrasted in this research. The rule inference procedure, membership functions, aggregation procedures, and defuzzification strategies used in each model are all covered. It also outlines the advantages and disadvantages of both models in terms of handling uncertainty, computational efficiency, interpretability, and system modeling.
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Akram, M., Alsulami, S., and Zahid, K. (2021). A Hybrid Method for Complex Pythagorean Fuzzy Decision Making. Mathematical Problems in Engineering, 2021, Article 9915432. https://doi.org/10.1155/2021/9915432
Castillo, O., and Melin, P. (2021). A New Fuzzy Fractal Control Approach of Non-Linear Dynamic Systems: The Case of Controlling the COVID-19 Pandemics. Chaos, Solitons and Fractals, 151, 111250. https://doi.org/10.1016/j.chaos.2021.111250
Chen, H. X., Fu, S., and Wang, H. (2021). Optical Coherence Tomographic Image Denoising Based on Chi-Square Similarity and Fuzzy Logic. Optics and Laser Technology, 143, 107298. https://doi.org/10.1016/j.optlastec.2021.107298
Du, X., et al. (2021). Assessing the Adequacy of Hemodialysis Patients Via the Graph-Based Takagi–Sugeno–Kang Fuzzy System. Computational and Mathematical Methods in Medicine, 2021, Article 9036322. https://doi.org/10.1155/2021/9036322
Gao, X. Y. Y., Ali, A. A., Hassan, H. S., and Anwar, E. M. (2021). Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method. Complexity, 2021, Article 6663455. https://doi.org/10.1155/2021/6663455
Nivethitha, T., Palanisamy, S. K., Prakash, K. M., and Jeevitha, K. (2021). Comparative Study of ANN and Fuzzy Classifier for Forecasting Electrical Activity of Heart to Diagnose COVID-19. Materials Today: Proceedings, 45, 2293–2305. https://doi.org/10.1016/j.matpr.2020.10.400
Roumeliotis, S., et al. (2021). Oxidative Stress Genes in Diabetes Mellitus Type 2: Association with Diabetic Kidney Disease. Oxidative Medicine and Cellular Longevity, 2021, Article 2531062. https://doi.org/10.1155/2021/2531062
Safi, H., Nourinia, R., Safi, S., Hadian, E., Kheiri, B., and Ahmadieh, H. (2021). Retinal Vascular Response to Hyperoxia in Patients with Diabetes Mellitus without Diabetic Retinopathy. Journal of Ophthalmology, 2021, Article 9877205. https://doi.org/10.1155/2021/9877205
Thakkar, H., Shah, V., Yagnik, H., and Shah, M. (2021). Comparative Anatomization of Data Mining and Fuzzy Logic Techniques Used in Diabetes Prognosis. Clinical eHealth, 4, 12–23. https://doi.org/10.1016/j.ceh.2020.11.001
Tsegaw, A., et al. (2021). Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients Attending the Diabetic Clinic of the University of Gondar Hospital, Northwest Ethiopia. Journal of Ophthalmology, 2021, Article 6696548. https://doi.org/10.1155/2021/6696548
Ullah, H., Youn, H. Y., and Han, Y. H. (2021). Integration of Type-2 Fuzzy Logic and Dempster–Shafer Theory for Accurate Inference of IOT-Based Health-Care System. Future Generation Computer Systems, 124, 369–380. https://doi.org/10.1016/j.future.2021.06.012
Wardana, H. K., Ummah, I., and Fitriyah, L. A. (2020). Mamdani Fuzzy Inference System (FIS) for Early Diagnosis of Diabetes Mellitus (DM) and Calorie Needs. Advances in Engineering Research, 196, 387–394. https://doi.org/10.2991/aer.k.201124.070
Wu, J., et al. (2021). Diagnosis of Sleep Disorders in Traditional Chinese Medicine Based on Adaptive Neuro-Fuzzy Inference System. Biomedical Signal Processing and Control, 70, 102942. https://doi.org/10.1016/j.bspc.2021.102942
Yang, X., Han, X., Wen, Q., Qiu, X., Deng, H., and Chen, Q. (2021). Protective Effect of Keluoxin Against Diabetic Nephropathy in Type 2 Diabetes Mellitus Models. Evidence-Based Complementary and Alternative Medicine, 2021, Article 8455709. https://doi.org/10.1155/2021/8455709
Yap, M. H., et al. (2021). Deep Learning in Diabetic Foot Ulcers Detection: A Comprehensive Evaluation. Computers in Biology and Medicine, 135, 104596. https://doi.org/10.1016/j.compbiomed.2021.104596
Zheng, I., Yang, S., and Li, L. (2021). Aperiodic Sampled-Data Control for Chaotic System Based on Takagi–Sugeno Fuzzy Model. Complexity, 2021, Article 6401231. https://doi.org/10.1155/2021/6401231
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