SpringerLink
Log in

Applications of Fuzzy Logic, Artificial Neural Network and Neuro-Fuzzy in Industrial Engineering

  • Conference paper
  • First Online:
Industrial and Robotic Systems (LASIRS 2019)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 86))

Included in the following conference series:

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (Canada)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (Canada)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (Canada)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (Canada)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. 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

    Google Scholar 

  2. 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

  3. 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

    Google Scholar 

  4. Razvarz S, Jafari R (2017) ICA and ANN modeling for photocatalytic removal of pollution in wastewater. Math Comput Appl 22:38–48

    Google Scholar 

  5. 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

    Google Scholar 

  6. 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

    Article  Google Scholar 

  7. 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

    Google Scholar 

  8. 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

    Google Scholar 

  9. Jafari R, Razvarz S (2018) Solution of fuzzy differential equations using fuzzy sumudu transforms. Math Comput Appl 23(1):1–15

    MathSciNet  MATH  Google Scholar 

  10. 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

    Google Scholar 

  11. 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

    Google Scholar 

  12. 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

    Article  Google Scholar 

  13. 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

  14. 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

    Article  Google Scholar 

  15. 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

  16. 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

    Google Scholar 

  17. 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

  18. 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

    Google Scholar 

  19. Castellano G, Fanelli AM (2000) Variable selection using neural-network models. Neurocomputing 31:1–13

    Article  Google Scholar 

  20. Babuska R, Verbruggen H (2003) Neuro-fuzzy methods for nonlinear system identification. Annu Rev Control 27:73–85

    Article  Google Scholar 

  21. 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

    Google Scholar 

  22. 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

    Google Scholar 

  23. 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

    Google Scholar 

  24. Yasunobu S, Miyamoto S (1985) Automatic train operation system predictive fuzzy control. In: Sugeno M (ed) Industrial Applications of Fuzzy Control. North-Holland, Amsterdam

    Google Scholar 

  25. Wang LX (1994) Adaptive Fuzzy Systems and Control Design and Stability Analysis. Prentice Hall, NJ

    Google Scholar 

  26. Tarng YS, Tseng CM, Chung LK (1997) A fuzzy pulse discriminating system for electrical discharge machining. Int J Mach Tools Manuf 37:511–522

    Article  Google Scholar 

  27. 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

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. 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

    Google Scholar 

  30. 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

    Google Scholar 

  31. Wang LX, Mendel JM (1992) Fuzzy basis functions, universal approximation, and orthogonal least squares learning. IEEE Trans Neural Netw 3:807–814

    Article  Google Scholar 

  32. Jang J (1993) ANFIS: adaptive network-based fuzzy inference systems. IEEE Trans Syst Man Cybern 23:665–685

    Article  Google Scholar 

  33. 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

    Article  Google Scholar 

  34. 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

    Article  Google Scholar 

  35. Hossain MSJ, Ahmad N (2014) A neuro-fuzzy approach to select cutting parameters for commercial die manufacturing. Proc Eng 90:753–759

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Design, University of Leeds, Leeds, LS2 9JT, UK

    Raheleh Jafari

  2. School of Engineering and Science, Tecnológico de Monterrey, 89600, Altamira, Tampico, Mexico

    Miguel A. Contreras

  3. Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), 07360, Mexico City, Mexico

    Wen Yu

  4. School of Computing, University of Portsmouth, Buckingham Building, Portsmouth, PO1 3HE, UK

    Alexander Gegov

Authors
  1. Raheleh Jafari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miguel A. Contreras
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alexander Gegov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raheleh Jafari .

Editor information

Editors and Affiliations

  1. ESIME Ticoman, Instituto Politecnico Nacional, Mexico City, Mexico

    Eusebio E. Hernandez

  2. School of Engineering and Science, Tecnologico de Monterrey, Altamira, Mexico

    Sajjad Keshtkar

  3. CONACYT-CENTROGEO, Mexico City, Mexico

    S. Ivvan Valdez

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

Publish with us

Policies and ethics

Search

Navigation