Skip to main content
SpringerLink
Log in

Evolution of fuzzy logic: from intelligent systems and computation to human mind

  • Focus
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Inspired by human’s remarkable capability to perform a wide variety of physical and mental tasks without any measurements and computations and dissatisfied with classical logic as a tool for modeling human reasoning in an imprecise environment, Lotfi A. Zadeh developed the theory and foundation of fuzzy logic with his 1965 paper “Fuzzy sets” (Zadeh in Inf Control 8:378–53, 1965) and extended his work with his 2005 paper “Toward a generalized theory of uncertainty (GTU)—an outline” (Zadeh in Inf Control, 2005). Fuzzy logic has at least two main sources over the past century. The first of these sources was initiated by Peirce in the form what he called a logic of vagueness in 1900s, and the second source is Lotfi’s A. Zadeh work, fuzzy sets and fuzzy Logic in the 1960s and 1970s.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Black M (1937) Vagueness, an exercise in logical analysis. Philos Sci 6:427–55

    Article  Google Scholar 

  • Brock JE (1979) Principle themes in Peirce’s logic of vagueness. In Peirce Studies 1. Lubbock: Institute for Studies in Pragmaticism, pp 41–50

  • Engel-Tiercelin C (1992) Vagueness and the unity of C.S. Peirce’s realism. Trans C.S. Peirce Soc 28(1):51–82

    Google Scholar 

  • Green B (2005) One hundred years of uncertainty, NYT, Op-Ed 2818 words, late edition—final, Section A, Column 1, p 27

  • Lukaszewicz W (1990) Non-monotonic reasoning. Ellis Harwood, Chichester

    Google Scholar 

  • Merrell F (1995) Semiosis in the postmodern age. Purdue University Press, West Lafayette

    Google Scholar 

  • Merrell F (1996) Signs grow: semiosis and life processes. University of Toronto Press, Toronto

    Google Scholar 

  • Merrell F (1997) Peirce, signs, and meaning. University of Toronto Press, Toronto

    Google Scholar 

  • Merrell F (1998) Sensing semiosis: toward the possibility of complementary cultural ‘Logics’. St. Martin’s Press, New York

    Google Scholar 

  • Nadin M (1982) Consistency, completeness and the meaning of sign theories. Am J Semiot l(3):79–98

    Google Scholar 

  • Nadin M (1983) The logic of vagueness and the category of synechism. In: Freeman E (ed) The relevance of Charles Peirce. Monist Library of Philosophy, LaSalle, pp l54–166

    Google Scholar 

  • Peirce CS (1908) CP 6.475, CP 6.458, CP 6.461 [CP refers to Collected Papers of Charles Sanders Peirce, Hartshorne C, Weiss P, Burks AW (eds) Harvard University Press, Cambridge, Mass, 1931–58; the first refers to the volum, the number after the dot to the paragraph, and the last number to the year of the text]

  • Russell B (1923) Vagueness. Aust J Philos 1:88–91

    Google Scholar 

  • Savage L (1954) The foundations of statistics. Wiley, New York

    MATH  Google Scholar 

  • Scaruffi P (2003) Thinking about thought. iUniverse publishing, Lincoln

    Google Scholar 

  • Shafer G (1976) A mathematical theory of evidence. Princeton Univ Press, New Jersey

    MATH  Google Scholar 

  • Zadeh LA (1950) An extension of Wiener’s theory of prediction, (with J. R. Ragazzini). J Appl Phys 21:645–655

    Article  Google Scholar 

  • Zadeh LA (1952) The analysis of sampled-data systems, (with J. R. Ragazzini). Appl Ind (AIEE) 1:224–234

    Google Scholar 

  • Zadeh LA (1963) Linear system theory-the state space approach, (co-authored with C. A. Desoer). McGraw-Hill Book Co., New York

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:378–53

    Article  Google Scholar 

  • Zadeh LA (1970) Decision-making in a fuzzy environment, (with R. E. Bellman). Manage Sci 17:B-141–B-164

    Google Scholar 

  • Zadeh LA (1972) Fuzzy languages and their relation to human and machine intelligence. In: Proceedings of international conference on man and computer, Bordeaux, France, pp 130–165

  • Zadeh LA (1975) Fuzzy logic and approximate reasoning. Synthese 30:407–28

    Article  MATH  Google Scholar 

  • Zadeh LA (1979) A theory of approximate reasoning. In: Hayes J, Michie D, Mikulich LI (eds) Machine Intelligence, vol 9. Halstead Press, New York, pp 149–194

    Google Scholar 

  • Zadeh LA (1992) Fuzzy Logic for the management of uncertainty. In: Zadeh LA, Kacprzyk J (eds) Wiley, New York

  • Zadeh LA (1999) From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions. IEEE Trans Circuits Syst 45:105–119

    Google Scholar 

  • Zadeh LA (2005) Toward a generalized theory of uncertainty (GTU)—an outline. Inf Sci

  • Zadeh, Lotfi A (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cyber SMC-3:28–44

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. BISC Program, Computer Sciences Division, EECS Department, University of California, Berkeley, USA

    Masoud Nikravesh

  2. NERSC-Lawrence Berkeley National Lab, California, 94720-1776, USA

    Masoud Nikravesh

Authors
  1. Masoud Nikravesh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Masoud Nikravesh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Nikravesh, M. Evolution of fuzzy logic: from intelligent systems and computation to human mind. Soft Comput 12, 207–214 (2008). https://doi.org/10.1007/s00500-007-0192-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-007-0192-9

Keywords

Search

Navigation