Abstract
Artificial intelligence methods namely artificial neural network, fuzzy logic, and neuro-fuzzy have been effectively utilized in different applications like business, marketing, control engendering, health care, and social services. To demonstrate the usage of fuzzy set theory, artificial neural network, as well as neuro-fuzzy in industrial engineering and also for providing a basis for future investigation, a literature review of the artificial neural network, fuzzy logic, and neuro-fuzzy in industrial engineering is conducted in this paper.
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References
Razvarz S, Jafari R, Yu W, Khalili A (2017) PSO and NN modeling for photocatalytic removal of pollution in wastewater. In: 14th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE) Electrical Engineering, pp 1–6
Jafari R, Yu W (2015) Artificial neural network approach for solving strongly degenerate parabolic and burgers-fisher equations In: 12th International Conference on Electrical Engineering, Computing Science and Automatic Control. https://doi.org/10.1109/iceee.2015.7357914
Jafari R, Razvarz S, Gegov A (2018) A new computational method for solving fully fuzzy nonlinear systems. In: Computational Collective Intelligence, ICCCI 2018. Lecture Notes in Computer Science, vol 11055. Springer, Cham, pp 503–512
Razvarz S, Jafari R (2017) ICA and ANN modeling for photocatalytic removal of pollution in wastewater. Math Comput Appl 22:38–48
Razvarz S, Jafari R, Gegov A, Yu W, Paul S (2018) Neural network approach to solving fully fuzzy nonlinear systems. In: Fuzzy modeling and control Methods Application and Research, Nova science publisher Inc., New York, pp 45–68. ISBN: 978-1-53613- 415-5
Razvarz S, Jafari R (2017) Intelligent techniques for photocatalytic removal of pollution in wastewater. J Electr Eng 5:321–328. https://doi.org/10.17265/2328-2223/2017.06.004
Jafari R, Razvarz S (2017) solution of fuzzy differential equations using fuzzy Sumudu transforms. In: IEEE International Conference on Innovations in Intelligent Systems and Applications, pp 84–89
Jafari R, Razvarz S, Gegov A, Paul S (2018) Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers. In: Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, 5–7 September 2018, Nottingham, UK. Advances in Intelligent Systems and Computing, vol 840. Springer, Cham, pp 66-107
Jafari R, Razvarz S (2018) Solution of fuzzy differential equations using fuzzy sumudu transforms. Math Comput Appl 23(1):1–15
Jafari R, Razvarz S, Gegov A (2019) Solving differential equations with Z-Numbers by utilizing fuzzy Sumudu transform, intelligent systems and applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham, pp 1125–1138
Yu W, Jafari R (2019) Fuzzy modeling and control with fuzzy equations and Z-number, IEEE Press Series on Systems Science and Engineering, Wiley-IEEE Press. ISBN-13: 978-1119491552
Razvarz S, Jafari R (2018) Experimental study of Al2O3 nanofluids on the thermal efficiency of curved heat pipe at different tilt angle. J Nanomater 2018:1–7
Razvarz S, Vargas-Jarillo C, Jafari R (2019) Pipeline monitoring architecture based on observability and controllability analysis. In: IEEE International Conference on Mechatronics (ICM), Ilmenau, Germany, vol 1, pp 420–423. https://doi.org/10.1109/icmech.2019.872287
Razvarz S, Vargas-jarillo C, Jafari R, Gegov A (2019) Flow control of fluid in pipelines using PID controller. IEEE Access 7:25673–25680. https://doi.org/10.1109/ACCESS.2019.2897992
Jafari R, Yu W, Razvarz S, Gegov A (2019) Numerical methods for solving fuzzy equations: a Survey. In: Fuzzy Sets and Systems. https://doi.org/10.1016/j.fss.2019.11.003. ISSN 0165–0114
Jafari R, Yu W, Li X (2016) Solving fuzzy differential equation with Bernstein neural networks. In: IEEE International Conference on Systems, Man, and Cybernetics, Budapest, Hungary, pp 1245–1250
Jafari R, Yu W (2017) Uncertain nonlinear system control with fuzzy differential equations and Z-numbers. In: 18th IEEE International Conference on Industrial Technology, Canada, pp 890–895. https://doi.org/10.1109/icit.2017.7915477
Jafarian A, Measoomy NS, Jafari R (2012) Solving fuzzy equations using neural nets with a new learning algorithm. J Adv Comput Res 3:33–45
Castellano G, Fanelli AM (2000) Variable selection using neural-network models. Neurocomputing 31:1–13
Babuska R, Verbruggen H (2003) Neuro-fuzzy methods for nonlinear system identification. Annu Rev Control 27:73–85
Murakami S, Maeda M (1985) Automobile speed control system using a fuzzy logic controller. In: Sugeno M (ed) Industrial Applications of Fuzzy Control. North-Holland, Amsterdam
Scharf EM, Mandix NJ (1985) The application of a fuzzy controller to the control of a multi-degree-of-freedom robot arm. In: Sugeno M (ed) Industrial Applications of Fuzzy Control. North-Holland, Amsterdam
Yagishita O, Itoh O, Sugeno M (1985) Application of fuzzy reasoning to the water purification process. In: Sugeno M (ed) Industrial Applications of Fuzzy Control. North-Holland, Amsterdam
Yasunobu S, Miyamoto S (1985) Automatic train operation system predictive fuzzy control. In: Sugeno M (ed) Industrial Applications of Fuzzy Control. North-Holland, Amsterdam
Wang LX (1994) Adaptive Fuzzy Systems and Control Design and Stability Analysis. Prentice Hall, NJ
Tarng YS, Tseng CM, Chung LK (1997) A fuzzy pulse discriminating system for electrical discharge machining. Int J Mach Tools Manuf 37:511–522
Taskin H, Cemalettin K, Uygun O, Arslankaya S (2006) Fuzzy logic control of a fluid catalytic cracking unit (FCCU) to improve dynamic performance. Comput Chem Eng 30:850–863
Rafiee J, Arvani F, Harifi A, Sadeghi MH (2006) Intelligent condition monitoring of a gearbox using artificial neural network. Mech Syst Signal Process 21(4):1746–1754
Arriagada J, Genrup M, Assadi M, Loberg A (2003) Fault Diagnosis System for an Industrial Gas Turbine by Means of Neural Networks. In: Proceedings of International Gas Turbine Congress, Tokyo, Japan
Bersini H, Nordvik J, Bonarini A (1993) A simple direct adaptive fuzzy controller derived from its neural equivalent. In: Proceedings 1993 IEEE International Conference on Fuzzy System, vol 1, pp 345–350
Wang LX, Mendel JM (1992) Fuzzy basis functions, universal approximation, and orthogonal least squares learning. IEEE Trans Neural Netw 3:807–814
Jang J (1993) ANFIS: adaptive network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23:665–685
Khajeh A, Modarress H, Rezaee B (2009) Application of adaptive neuro-fuzzy inference system for solubility prediction of carbon dioxide in polymers. Expert Syst Appl 36:5728–5732
Yazdi MRS, Khorram A (2010) Modeling and optimization of milling process by using RSM and ANN methods. Int J Eng Technol (IJET) IACSIT 2:474–480
Hossain MSJ, Ahmad N (2014) A neuro-fuzzy approach to select cutting parameters for commercial die manufacturing. Proc Eng 90:753–759
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Jafari, R., Contreras, M.A., Yu, W., Gegov, A. (2020). Applications of Fuzzy Logic, Artificial Neural Network and Neuro-Fuzzy in Industrial Engineering. In: Hernandez, E., Keshtkar, S., Valdez, S. (eds) Industrial and Robotic Systems. LASIRS 2019. Mechanisms and Machine Science, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-030-45402-9_2
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