CIRP Encyclopedia of Production Engineering

2014 Edition
| Editors: The International Academy for Production Engineering, Luc Laperrière, Gunther Reinhart

Fuzzy Logic

Reference work entry
DOI: https://doi.org/10.1007/978-3-642-20617-7_6555

Definition

The term fuzzy logic has two different meanings. More specifically, in a narrow sense, fuzzy logic, FLn, is a logical system which may be viewed as an extension and generalization of classical multivalued logics. But in a wider sense, fuzzy logic, FLw, is almost synonymous with the theory of fuzzy sets (Zadeh 1975).

Extended Definition

Basically, fuzzy logic (FL) is a multivalued logic, which allows intermediate values to be defined between conventional evaluations like true/false, yes/no, and high/low. Fuzzy logic is an extension of the traditional logic to intermediate and approximate values.

Theory and Application

Introduction

The concept of fuzzy logic emerged in 1965 within the development of the theory of fuzzy sets by Lotfi A. Zadeh, professor of computer science at the University of California in Berkeley (Zadeh 1965).

Later, in 1972, Michio Sugeno of the Tokyo Institute of Technology introduced the concepts of fuzzy measure and fuzzy integral. One of the first...

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

References

  1. Araz ÖU, Salum L (2010) A multi-criteria adaptive control scheme based on neural networks and fuzzy inference for DRC manufacturing systems. Int J Prod Res 48(1):251–270MATHCrossRefGoogle Scholar
  2. Ayag Z, Özdemir RG (2006) A fuzzy AHP approach to evaluating machine tool alternatives. J Intell Manuf 17(2):179–190CrossRefGoogle Scholar
  3. Dotoli M, Fanti MP, Mangini AM (2007) Fuzzy multi-objective optimization for network design of integrated e-supply chains. Int J Comput Integ Manuf 20(6):588–601CrossRefGoogle Scholar
  4. Eversheim W, Hack T (1996) Managing multiple product variants in assembly control with a fuzzy Petri-Net approach. CIRP Ann Manuf Technol 45(1):45–48CrossRefGoogle Scholar
  5. Homayouni SM, Hong TS, Ismail N (2009) Development of genetic fuzzy logic controllers for complex production systems. Comput Ind Eng 57:1247–1257CrossRefGoogle Scholar
  6. Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications, 1st edn. Prentice Hall, Upper Saddle River, p 592MATHGoogle Scholar
  7. Ko TJ, Cho DW, Lee JM (1992) Fuzzy pattern recognition for tool wear monitoring in diamond turning. CIRP Ann Manuf Technol 41(1):125–128CrossRefGoogle Scholar
  8. Mamdani EH (1974) Application of fuzzy algorithms for control of simple dynamic plant. IEEE 121(12):1585–1588Google Scholar
  9. Monfared MAS, Yang JB (2004) Design of an intelligent manufacturing scheduling and control system using fuzzy logic: sensitivity analysis and parameter optimization. J Intell Fuzzy Syst 15(2):89–104MATHGoogle Scholar
  10. Monitto M, Pappalardo P, Tolio T (2002) A new fuzzy AHP method for the evaluation of automated manufacturing systems. CIRP Ann Manuf Technol 51(1):395–398CrossRefGoogle Scholar
  11. Monostori L, Egresits C (1994) Modelling and monitoring of milling through neuro-fuzzy techniques. In: Second IFAC/IFIP/IFORS workshop on intelligent manufacturing systems, 13–15 June 1994, Vienna pp 381–386Google Scholar
  12. Nikravesh M, Loia V, Azvine B (2002) Fuzzy Logic and the Internet (FLINT): Internet, World Wide Web, and search engines. Int J Soft Comput 6(Spec Iss):287–299MATHCrossRefGoogle Scholar
  13. Noto la Diega S, Passannanti A, Perrone G (1995) Machining economics in FMS by a fuzzy approach. CIRP Ann Manuf Technol 44(1):417–420CrossRefGoogle Scholar
  14. Onut S, Kara SS, Mert S (2009) Selecting the suitable material handling equipment in the presence of vagueness. Int J Adv Manuf Technol 44(7–8):818–828CrossRefGoogle Scholar
  15. Pal SK, Mitra P (2007) Pattern recognition algorithms for data mining: scalability, knowledge discovery, and soft granular computing. Chapman & Hall, WashingtonMATHGoogle Scholar
  16. Perrone G, Zinno A, Noto La Diega S (2001) Fuzzy discrete event simulation: a new tool for rapid analysis of production systems under vague information. J Intell Manuf 12(3):309–326CrossRefGoogle Scholar
  17. Petrovic PB, Milacic VR (1998) A concept of an intelligence fuzzy control for assembly robot. CIRP Ann 47(1):9CrossRefGoogle Scholar
  18. Porter B, Zadeh NN, Chisholm AWJ (1997) Evolutionary design of fuzzy-logic controllers for manufacturing systems. CIRP Ann Manuf Technol 46(1):425–428CrossRefGoogle Scholar
  19. Qiao B, Zhu JY (2000) Fuzzy modeling of inverse dynamics for robotic manipulators based on genetic algorithm. CIRP Ann Manuf Technol 49(1):321–324CrossRefGoogle Scholar
  20. Ren Q, Balazinski M, Baron L, Jemielniak K (2011) TSK fuzzy modeling for tool wear condition in turning processes: an experimental study. Eng Appl Artif Intel 24(2):260–265CrossRefGoogle Scholar
  21. Ross TJ (2004) Fuzzy logic with engineering applications, 2nd edn. Wiley, ChichesterMATHGoogle Scholar
  22. Russell SJ, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. Prentice Hall, Upper Saddle RiverMATHGoogle Scholar
  23. Sugeno M (1985) Industrial applications of fuzzy control. Elsevier Science, New YorkMATHGoogle Scholar
  24. Teti R, D’Addona D (2003) Grinding wheel management through neuro-fuzzy forecasting of dressing cycle time. CIRP Ann Manuf Technol 52(1):407–410CrossRefGoogle Scholar
  25. Teti R, Kumara SRT (1997) Intelligent computing methods for manufacturing systems. CIRP Ann 46(2):629–652CrossRefGoogle Scholar
  26. Yuniarto MN, Labib AW (2005) Optimal control of an unreliable machine using fuzzy-logic control: from design to implementation. Int J Prod Res 43(21):4509–4537MATHCrossRefGoogle Scholar
  27. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353MathSciNetMATHCrossRefGoogle Scholar
  28. Zadeh LA (1975) Fuzzy logic and approximate reasoning. Synthese 30(3–4):407–428MATHCrossRefGoogle Scholar
  29. Zhang HC, Li J, Merchant ME (2003) Using fuzzy multi-agent decision-making in environmentally conscious supplier management. CIRP Ann Manuf Technol 52(1):385–388CrossRefGoogle Scholar
  30. Zhao DB, Zhu JY (1995) A fuzzy variable structure adaptive control design for robot manipulators. CIRP Ann Manuf Technol 44(1):363–368CrossRefGoogle Scholar
  31. Zhu JY, Zhang YZ (1987) An expert system of the part classification. CIRP Ann Manuf Technol 36(1):351–353CrossRefGoogle Scholar
  32. Zukin M, Young RE (2001) Applying fuzzy logic and constraint networks to a problem of manufacturing flexibility. Int J Prod Res 39(14):3253–3273MATHCrossRefGoogle Scholar

Copyright information

© CIRP 2014

Authors and Affiliations

  1. 1.Fraunhofer Joint Laboratory of Excellence on Advanced Production Technology, Department of Chemical, Materials and Production EngineeringUniversity of Naples Federico IINaplesItaly