[1] Novák, V., Perfilieva, I., Mockor, J., Mathematical Principles of Fuzzy Logic, Springer Science & Business Media, 1999.
[2] Zadeh, L.A., Klir, G.J., Yuan, B., Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers, World Scientific, 1996.
[3] Jang, J.-S.R., Sun, C.-T., Mizutani, E., Neuro-fuzzy and soft computing – a computational approach to learning and machine intelligence, IEEE Transactions on Automatic Control, 42(10), 1997, 1482–1484.
[4] Nauck, D., Kruse, R., Neuro-fuzzy systems for function approximation, Fuzzy Sets and Systems, 101(2), 1999, 261–271.
[5] Jane, J.B., Ganesh, E., A review on big data with machine learning and fuzzy logic for better decision making, International Journal of Scientific & Technology Research, 8(10), 2019, 1221–1225.
[6] Rengasamy, D., Mase, J.M., Kumar, A., Rothwell, B., Torres, M.T., Alexander, M.R., Winkler, D.A., Figueredo, G.P., Feature importance in machine learning models: A fuzzy information fusion approach, Neurocomputing, 511, 2022, 163–174.
[7] Rezaeipanah, A., Mojarad, M., Fakhari, A., Providing a new approach to increase fault tolerance in cloud computing using fuzzy logic, International Journal of Computers and Applications, 44(2), 2022, 139–147.
[8] Vankdothu, R., Hameed, M.A., Brain tumor segmentation of MR images using SVM and fuzzy classifier in machine learning, Measurement: Sensors, 24, 2022, 100440.
[9] Kumar, T., Sankaran, K.S., Ritonga, M., Asif, S., Sathiya Kumar, C., Mohammad, S., Sengan, S., Asenso, E., Fuzzy logic and machine learning-enabled recommendation system to predict suitable academic program for students, Mathematical Problems in Engineering, 2022(1), 2022, 5298468.
[10] Kahraman, C., et al., Intelligent and Fuzzy Systems: Artificial Intelligence in Human-Centric, Resilient and Sustainable Industries, Proceedings of the INFUS 2025 Conference, Vol. 4, Springer Nature, 2025.
[11] Gupta, K., Kumar, P., Upadhyaya, S., Poriye, M., Aggarwal, S., Fuzzy logic and machine learning integration: Enhancing healthcare decision-making, International Journal of Computer Information Systems and Industrial Management Applications, 16(3), 2024, 20–20.
[12] Das, N., Saha, S., Nasipuri, M., Basu, S., Chakraborti, T., Deep-Fuzz: A synergistic integration of deep learning and fuzzy water flows for fine-grained nuclei segmentation in digital pathology, PLOS ONE, 18(6), 2023, e0286862.
[13] Hasan, D.F., Khidhir, A., Toward enhancement of deep learning techniques using fuzzy logic: a survey, International Journal of Electrical and Computer Engineering, 13(3), 2023, 3041–3055.
[14] Yazdinejad, A., Dehghantanha, A., Parizi, R.M., Epiphaniou, G., An optimized fuzzy deep learning model for data classification based on NSGA-II, Neurocomputing, 522, 2023, 116–128.
[15] Kamthan, S., Singh, H., Meitzler, T., Hierarchical fuzzy deep learning for image classification, Memories-Materials, Devices, Circuits and Systems, 2, 2022, 100016.
[16] Sierra-Garcia, J.E., Santos, M., Deep learning and fuzzy logic to implement a hybrid wind turbine pitch control, Neural Computing and Applications, 34(13), 2022, 10503–10517.
[17] Mazhar, A., Tanveer, A., Izhan, M., Khan, M.Z.T., Robust control approaches and trajectory planning strategies for industrial robotic manipulators in the era of Industry 4.0: A comprehensive review, Engineering Proceedings, 56(1), 2023, 75.
[18] Ali, I., Hassan, M., Bano, Z., Jawo, E., Almazah, M.M., Robust control of robot manipulator dynamics with two stages algorithm of optimal and integral sliding mode approaches, Scientific Reports, 15(1), 2025, 36585.
[19] Mohamadi, S.R., Khorashadizadeh, S., Adaptive fuzzy control of time-varying impedance in rehabilitation exercises, Transactions of the Institute of Measurement and Control, 47(5), 2025, 854–868.
[20] Saeedi, B., Sadedel, M., Mohammadi Moghaddam, M., Innovative integration of meta-heuristic algorithms with adaptive TSK fuzzy systems for inverse kinematics in a new wrist and forearm rehabilitation exoskeleton, Iranian Journal of Science and Technology, Transactions of Mechanical Engineering, 49(2), 2025, 927–962.
[21] Toro-Ossaba, A., Tejada, J.C., Sanin-Villa, D., Myoelectric control in rehabilitative and assistive soft exoskeletons: A comprehensive review of trends, challenges, and integration with soft robotic devices, Biomimetics, 10(4), 2025, 214.
[22] Yang, L., Shi, Z., Jia, R., Kou, J., Du, M., Bian, C., Wang, J., Multi-branch deep learning neural network prediction model for the development of angular biosensors based on sEMG, Frontiers in Bioengineering and Biotechnology, 12, 2024, 1492232.
[23] Maideen, A., Mohanarathinam, A., Deep learning-based torque and joint angle estimation from electromyography signals: A hybrid DNN-LSTM approach, KSII Transactions on Internet and Information Systems, 19(8), 2025, 2529–2547.
[24] Rojas-Martínez, M., Serna, L.Y., Jordanic, M., Marateb, H.R., Merletti, R., Mañanas, M.A., High-density surface electromyography signals during isometric contractions of elbow muscles of healthy humans, Scientific Data, 7(1), 2020, 397.
[25] Shakeriaski, F., Mohammadian, M., Evaluation of different controllers for sensing-based movement intention estimation and safe tracking in a simulated LSTM network-based elbow exoskeleton robot, Sensors, 26(2), 2026, 387.
[26] Tanaka, T., Nambu, I., Maruyama, Y., Wada, Y., Sliding-window normalization to improve the performance of machine-learning models for real-time motion prediction using electromyography, Sensors, 22(13), 2022, 5005.
[27] Han, J., Wang, F., Sun, C., Trajectory tracking control of a manipulator based on an adaptive neuro-fuzzy inference system, Applied Sciences, 13(2), 2023, 1046.
[28] Salami, M.-J.E., Bulale, Y.I., Design and implementation of fuzzy-based PID controller, in Proceedings of the 2002 IEEE International Conference on Industrial Technology (ICIT), Vol. 1, IEEE, 2002, 220–225.
Journal of Applied and Computational Mechanics