[1] C. T. Lin, and C. S. G. Lee, "Neural-network-based fuzzy logic control and decision system", IEEE Transactions on computers, Vol. 40, No. 12, 1991, pp. 1320-1336.
[2] L. A. Zadeh, "The concept of a linguistic variable and its application to approximate reasoning—I", Information sciences, Vol. 8, No. 3, 1975, pp. 199-249.
[3] H. Chaoui, M. Khayamy, and A. A. Aljarboua, "Adaptive interval type‐2 fuzzy logic control for PMSM drives with a modified reference frame", IEEE Transactions on Industrial Electronics, 2017, Vol. 64, No. 5, pp. 3786–3797.
[4] R. Coteli, H. Acikgoz, F. Ucar, and B. Dandil, "Design and implementation of type‐2 fuzzy neural system controller for PWM rectifiers", International Journal of Hydrogen Energy, Vol. 42, No. 32, 2017, pp. 20759–20771.
[5] F. Chao, D. Zhou, C. M. Lin, L. Yang, C. Zhou, and C. Shang, "Type-2 fuzzy hybrid controller network for robotic systems", IEEE transactions on cybernetics, July 2019.
[6] H. Wang, C. Luo, and X. Wang, "Synchronization and identification of nonlinear systems by using a novel self-evolving interval type-2 fuzzy LSTM-neural network", Engineering Applications of Artificial Intelligence, Vol. 81, 2019, pp. 79-93.
[7] K. Dahal, K. Almejalli, M. A. Hossain, and W. Chen, "GA‐based learning for rule identification in fuzzy neural networks", Applied Soft Computing, Vol. 35, 2015, pp. 605–617.
[8] N. Togun, and S. Baysec, "Nonlinear identification of a spark ignition engine torque based on ANFIS with NARX method", Expert Systems, Vol. 33, No. 6, 2016, pp. 559–568.
]۹[ فرزانه میراخورلو و ابراهیم نجفی کانی، "بررسی و پیشبینی خواص فیزیکی و مکانیکی کامپوزیت کاه و گچ به کمک مدل شبکه استنتاج عصبی فازی تطبیقی"، نشریه مدلسازی در مهندسی، دوره ۱۷، شماره ۵۸، پاییز ۱۳۹۸، صفحه ۲۶۷-۲۷۸.
]۱۰[ حسین قنادزاده گیلانی، الهیار داغبندان، محمد اکبری زاده و میثم آزادیان، "مدلسازی سیستمهای تعادلی بخار- مایع و مایع - مایع با استفاده از مدلهای ترمودینامیکی، ساختارهای فازی و شبکه های عصبی نوعGMDH، نشریه مدلسازی در مهندسی، دوره ۱۶، شماره ۵۵، زمستان ۱۳۹۷، صفحه ۱۹-۳۳.
]۱۱[ نعیمه باقری راد و جواد بهنامیان، "انتخاب تأمینکننده با استفاده از رویکرد ترکیبی ANP-DEMATEL-VIKOR فازی"، نشریه مدل سازی در مهندسی، دوره ۱۸، شماره ۶۰، بهار ۱۳۹۹.
[12] S. Hassan, M. A. Khanesar, E. Kayacan, J. Jaafar, and A. Khosravi, "Optimal design of adaptive type‐2 neuro‐fuzzy systems: A review", Applied Soft Computing, Vol. 44, 2016, pp. 134–143.
[13] W. Zhao, K. Li, and G. W. Irwin, "A new gradient descent approach for local learning of fuzzy neural models", IEEE Transactions on Fuzzy Systems, Vol. 21, No. 1, 2013, pp. 30–44.
[14] S. Huang, and M. Chen, "Constructing optimized interval type‐2 TSK neuro‐fuzzy systems with noise reduction property by quantum inspired BFA", Neurocomputing, Vol. 173, 2016, pp. 1839–1850.
[15] J. F. De Canete, A. Garcia‐Cerezo, I. García‐Moral, P. Del Saz, and E. Ochoa, "Object‐oriented approach applied to ANFIS modeling and control of a distillation column", Expert Systems with Applications, Vol. 40, No. 14, 2013, pp. 5648–5660.
[16] J. S. R. Jang, and E. Mizutani, "Levenberg‐Marquardt method for ANFIS learning", Proceedings of North American Fuzzy Information Processing, Biennial Conference of the North American IEEE, June 1996, pp. 87‐91.
[17] A. A. Ibrahim, H. B. Zhou, S. X. Tan, C. L. Zhang, and J. A. Duan, "Regulated Kalman filter based training of an interval type-2 fuzzy system and its evaluation", Engineering Applications of Artificial Intelligence, Vol. 95, 2020, p. 103867.
[18] I. Eyoh, R. John, G. De Maere, and E. Kayacan, "Hybrid learning for interval type-2 intuitionistic fuzzy logic systems as applied to identification and prediction problems", IEEE Transactions on Fuzzy Systems, Vol. 26, No. 5, 2018, pp. 2672-2685.
[19] A. Sarkheyli, A. M. Zain, and S. Sharif, "Robust optimization of ANFIS based on a new modified GA", Neurocomputing, Vol. 166, 2015, pp. 357–366.
[20] M. Elloumi, M. Krid, D. S. Masmoudi, "Neuro‐fuzzy system based on particle swarm optimization algorithm for image denoising application", International Conference on Advances in Biomedical Engineering (ICABME), International Conference on IEEE, September 2015, pp. 9‐12.
[21] V. S. Ghomsheh, M. A. Shoorehdeli, and M. Teshnehlab, "Training ANFIS structure with modified PSO algorithm", Proceedings of Mediterranean Conference on Control and Automation, July 2007, pp. 1‐6.
[22] Y. Maldonado, O. Castillo, and P. Melin, "Particle swarm optimization of interval type‐2 fuzzy systems for FPGA applications", Applied Soft Computing, Vol. 13, No. 1, 2013, pp. 496–508.
[23] A. Bagis, and M. Konar, "Comparison of Sugeno and Mamdani fuzzy models optimized by artificial bee colony algorithm for nonlinear system modelling", Transactions of the Institute of Measurement and Control, 2016.
[24] C. F. Juang, C. W. Hung, and C. H. Hsu, "Rule‐based cooperative continuous ant colony optimization to improve the accuracy of fuzzy system design", IEEE Transactions on Fuzzy Systems, Vol. 22, No. 4, 2014, pp. 723–735.
[25] M. Almaraashi, R. John, A. Hopgood, and S. Ahmadi, "Learning of interval and general type‐2 fuzzy logic systems using simulated annealing: Theory and practice", Information Sciences, Vol. 360, 2016, pp. 21–42.
[26] S. M. A. Pahnehkolaei, A. Alfi, A. Sadollah, and J. H. Kim, "Gradient‐based water cycle algorithm with evaporation rate applied to chaos suppression", Applied Soft Computing, Vol. 53, 2017, pp. 420–440.
[27] Q. Liang, and J. M. Mendel, "Equalization of nonlinear time‐varying channels using type‐2 fuzzy adaptive filters", IEEE Transactions on Fuzzy Systems, Vol. 8, No. 5, 2000, pp. 551–563.
[28] E. Kayacan, E. Kayacan, and M. A. Khanesar, "Identification of nonlinear dynamic systems using type‐2 fuzzy neural networks—A novel learning algorithm and a comparative study", IEEE Transactions on Industrial Electronics, Vol. 62, No. 3, 2015, pp. 1716–1724.
[29] M. A. Shoorehdeli, M. Teshnehlab, and A. K. Sedigh, "Identification using ANFIS with intelligent hybrid stable learning algorithm approaches", Neural Computing and Applications, Vol. 18, No. 2, 2009, pp. 157–174.
[30] J. Tavoosi, A. A. Suratgar, and M. B. Menhaj, "Stable ANFIS2 for nonlinear system identification", Neurocomputing, Vol. 182, 2016, pp. 235-246.
[31] J. Tavoosi, A. A. Suratgar, and M. B. Menhaj, "Stability analysis of recurrent type-2 TSK fuzzy systems with nonlinear consequent part", Neural Computing and Applications, Vol. 28, No. 1, 2017, pp. 47-56.
[32] G. M. M. De Los Angeles HernáNdez, "Hybrid learning mechanism for interval A2‐C1 type‐2 non‐singleton type‐2 Takagi–Sugeno–Kang fuzzy logic systems", Information Sciences, Vol. 220, 2013, pp. 149-169.
[33] C. H. Lu, and C. C. Tsai, "Generalized predictive control using recurrent fuzzy neural networks for industrial processes", Journal of Process Control, Vol. 17, No. 1, 2007, pp. 83–92.
[34] M. A. Shoorehdeli, M. Teshnehlab, and A. K. Sedigh, "Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter", Fuzzy Sets and Systems, Vol. 160, No. 7, 2009, pp. 922-948.
[35] Y. Y. Lin, J. Y. Chang, and C. T. Lin, "A TSK‐type‐based self‐evolving compensatory interval type‐2 fuzzy neural network (TSCIT2FNN) and its applications", IEEE Transactions on Industrial Electronics, Vol. 61, No. 1, 2013, pp. 447–459.
[36] M. M. Zabihi Shesh Poli, M. Aliyari Shoorehdeli, and A. Moarefianpour, "Stability analysis in identification of interval type‐2 adaptive neuro‐fuzzy inference system: Contribution to a novel Lyapunov function", Expert Systems, Vol. 36, No. 6, 2019, p. e12457.
[37] M. B. Begian, W. W. Melek, and J. M. Mendel, "Stability analysis of type-2 fuzzy systems", IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), 2008, pp. 947-953.
[38] J. Mendel, H. Hagras, W. W. Tan, W. W. Melek, and H. Ying, "Introduction to type-2 fuzzy logic control: theory and applications", John Wiley and Sons, 2014.
[39] J. M. Mendel, R. I. John, and F. Liu, "Interval type-2 fuzzy logic systems made simple", IEEE transactions on fuzzy systems, Vol. 14, No. 6, 2006, pp. 808-821.
[40] D. Wu, and J. M. Mendel, "Enhanced karnik--Mendel algorithms", IEEE transactions on fuzzy systems, Vol. 17, No. 4, 2008, pp. 923-934.
[41] M. A. Khanesar, E. Kayacan, M. Teshnehlab, and O. Kaynak, "Levenberg marquardt algorithm for the training of type-2 fuzzy neuro systems with a novel type-2 fuzzy membership function", IEEE symposium on advances in type-2 fuzzy logic systems, 2011, pp. 88-93.
[42] M. C. Mackey, and L. Glass, "Oscillation and chaos in physical control system. Science", Vol. 197, July 1977, pp. 287-289.