Fuzzy logic

Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.[1] By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.

The term fuzzy logic was introduced with the 1965 proposal of fuzzy set theory by Lotfi Zadeh.[2][3] Fuzzy logic had however been studied since the 1920s, as infinite-valued logic—notably by Łukasiewicz and Tarski.[4]

Fuzzy logic is based on the observation that people make decisions based on imprecise and non-numerical information. Fuzzy models or sets are mathematical means of representing vagueness and imprecise information (hence the term fuzzy). These models have the capability of recognising, representing, manipulating, interpreting, and utilising data and information that are vague and lack certainty.[5]

Fuzzy logic has been applied to many fields, from control theory to artificial intelligence.


Classical logic only permits conclusions which are either true or false. However, there are also propositions with variable answers, such as one might find when asking a group of people to identify a color. In such instances, the truth appears as the result of reasoning from inexact or partial knowledge in which the sampled answers are mapped on a spectrum.

Both degrees of truth and probabilities range between 0 and 1 and hence may seem similar at first, but fuzzy logic uses degrees of truth as a mathematical model of vagueness, while probability is a mathematical model of ignorance.[6]

Applying truth values

A basic application might characterize various sub-ranges of a continuous variable. For instance, a temperature measurement for anti-lock brakes might have several separate membership functions defining particular temperature ranges needed to control the brakes properly. Each function maps the same temperature value to a truth value in the 0 to 1 range. These truth values can then be used to determine how the brakes should be controlled.[7]

Linguistic variables

While variables in mathematics usually take numerical values, in fuzzy logic applications, non-numeric values are often used to facilitate the expression of rules and facts.[8]

A linguistic variable such as age may accept values such as young and its antonym old. Because natural languages do not always contain enough value terms to express a fuzzy value scale, it is common practice to modify linguistic values with adjectives or adverbs. For example, we can use the hedges rather and somewhat to construct the additional values rather old or somewhat young.

Fuzzification operations can map mathematical input values into fuzzy membership functions. And the opposite de-fuzzifying operations can be used to map a fuzzy output membership function into a "crisp" output value that can be then used for decision or control purposes.


  1. Fuzzify all input values into fuzzy membership functions.
  2. Execute all applicable rules in the rulebase to compute the fuzzy output functions.
  3. De-fuzzify the fuzzy output functions to get "crisp" output values.


Fuzzification is the process of assigning the numerical input of a system to fuzzy sets with some degree of membership. This degree of membership may be anywhere within the interval [0,1]. If it is 0 then the value does not belong to the given fuzzy set, and if it is 1 then the value completely belongs within the fuzzy set. Any value between 0 and 1 represents the degree of uncertainty that the value belongs in the set. These fuzzy sets are typically described by words, and so by assigning the system input to fuzzy sets, we can reason with it in a linguistically natural manner.

For example, in the image below the meanings of the expressions cold, warm, and hot are represented by functions mapping a temperature scale. A point on that scale has three "truth values"—one for each of the three functions. The vertical line in the image represents a particular temperature that the three arrows (truth values) gauge. Since the red arrow points to zero, this temperature may be interpreted as "not hot"; i.e. this temperature has zero membership in the fuzzy set "hot". The orange arrow (pointing at 0.2) may describe it as "slightly warm" and the blue arrow (pointing at 0.8) "fairly cold". Therefore, this temperature has 0.2 membership in the fuzzy set "warm" and 0.8 membership in the fuzzy set "cold". The degree of membership assigned for each fuzzy set is the result of fuzzification.

Fuzzy sets are often defined as triangle or trapezoid-shaped curves, as each value will have a slope where the value is increasing, a peak where the value is equal to 1 (which can have a length of 0 or greater) and a slope where the value is decreasing. They can also be defined using a sigmoid function.[9] One common case is the standard logistic function defined as

which has the following symmetry property

From this it follows that

Fuzzy logic operators

Fuzzy logic works with membership values in a way that mimics Boolean logic. To this end, replacements for basic operators AND, OR, NOT must be available. There are several ways to this. A common replacement is called the Zadeh operators:

Boolean Fuzzy
AND(x,y) MIN(x,y)
OR(x,y) MAX(x,y)
NOT(x) 1 – x

For TRUE/1 and FALSE/0, the fuzzy expressions produce the same result as the Boolean expressions.

There are also other operators, more linguistic in nature, called hedges that can be applied. These are generally adverbs such as very, or somewhat, which modify the meaning of a set using a mathematical formula.

However, an arbitrary choice table does not always define a fuzzy logic function. In the paper,[10] a criterion has been formulated to recognize whether a given choice table defines a fuzzy logic function and a simple algorithm of fuzzy logic function synthesis has been proposed based on introduced concepts of constituents of minimum and maximum. A fuzzy logic function represents a disjunction of constituents of minimum, where a constituent of minimum is a conjunction of variables of the current area greater than or equal to the function value in this area (to the right of the function value in the inequality, including the function value).

Another set of AND/OR operators is based on multiplication

x AND y = x*y
x OR y = 1-(1-x)*(1-y) = x+y-x*y

comes from this:

x OR y = NOT( AND( NOT(x), NOT(y) ) )
x OR y = NOT( AND(1-x, 1-y) )
x OR y = NOT( (1-x)*(1-y) )
x OR y = 1-(1-x)*(1-y)

IF-THEN rules

IF-THEN rules map input or computed truth values to desired output truth values. Example:

IF temperature IS very cold THEN fan_speed is stopped
IF temperature IS cold THEN fan_speed is slow
IF temperature IS warm THEN fan_speed is moderate
IF temperature IS hot THEN fan_speed is high

Given a certain temperature, the fuzzy variable hot has a certain truth value, which is copied to the high variable.

Should an output variable occur in several THEN parts, then the values from the respective IF parts are combined using the OR operator.


The goal is to get a continuous variable from fuzzy truth values.

This would be easy if the output truth values were exactly those obtained from fuzzification of a given number. Since, however, all output truth values are computed independently, in most cases they do not represent such a set of numbers. One has then to decide for a number that matches best the "intention" encoded in the truth value. For example, for several truth values of fan_speed, an actual speed must be found that best fits the computed truth values of the variables 'slow', 'moderate' and so on.

There is no single algorithm for this purpose.

A common algorithm is

  1. For each truth value, cut the membership function at this value
  2. Combine the resulting curves using the OR operator
  3. Find the center-of-weight of the area under the curve
  4. The x position of this center is then the final output.

Forming a consensus of inputs and fuzzy rules

Since the fuzzy system output is a consensus of all of the inputs and all of the rules, fuzzy logic systems can be well behaved when input values are not available or are not trustworthy. Weightings can be optionally added to each rule in the rulebase and weightings can be used to regulate the degree to which a rule affects the output values. These rule weightings can be based upon the priority, reliability or consistency of each rule. These rule weightings may be static or can be changed dynamically, even based upon the output from other rules.

Early applications

Many of the early successful applications of fuzzy logic were implemented in Japan. The first notable application was on the subway train in Sendai, in which fuzzy logic was able to improve the economy, comfort, and precision of the ride.[11] It has also been used in recognition of hand-written symbols in Sony pocket computers, flight aid for helicopters, controlling of subway systems in order to improve driving comfort, precision of halting, and power economy, improved fuel consumption for automobiles, single-button control for washing machines, automatic motor control for vacuum cleaners with recognition of surface condition and degree of soiling, and prediction systems for early recognition of earthquakes through the Institute of Seismology Bureau of Meteorology, Japan.[12]

Current applications

In medical decision making

Fuzzy logic is an important concept when it comes to medical decision making. Since medical and healthcare data can be subjective or fuzzy, applications in this domain have a great potential to benefit a lot by using fuzzy logic based approaches. One of the common application areas that use fuzzy logic is computer-aided diagnosis (CAD) in medicine.[13] CAD is a computerized set of inter-related tools which can be used to aid physicians in their diagnostic decision-making. For example, when a physician finds a lesion which is abnormal but still at a very early stage of development he/she may use a CAD approach to characterize the lesion and diagnose its nature. Fuzzy logic can be highly appropriate to describe key characteristics of this lesion. Fuzzy logic can be used in many different aspects within the CAD framework. Such aspects include in medical image analysis, biomedical signal analysis, segmentation of images or signals, and feature extraction / selection of images or signals as described, for instance, in [14][15][16][17] and[18].

The biggest question in this application area is how much useful information can be derived when using fuzzy logic. A major challenge is how to derive the required fuzzy data. This is even more challenging when one has to elicit such data from humans (usually, patients). As it said "The envelope of what can be achieved and what cannot be achieved in medical diagnosis, ironically, is itself a fuzzy one" [Seven Challenges, 2019]. How to elicit fuzzy data, and how to validate the accuracy of the data is still an ongoing effort strongly related to the application of fuzzy logic. The problem of assessing the quality of fuzzy data is a difficult one. This is why fuzzy logic is a highly promising possibility within the CAD application area but still requires more research to achieve its full potential.[19] Although the concepts of using fuzzy logic in CAD is exciting, there are still several challenges that fuzzy approaches face within the CAD framework.

Logical analysis

In mathematical logic, there are several formal systems of "fuzzy logic", most of which are in the family of t-norm fuzzy logics.

Propositional fuzzy logics

The most important propositional fuzzy logics are:

  • Monoidal t-norm-based propositional fuzzy logic MTL is an axiomatization of logic where conjunction is defined by a left continuous t-norm and implication is defined as the residuum of the t-norm. Its models correspond to MTL-algebras that are pre-linear commutative bounded integral residuated lattices.
  • Basic propositional fuzzy logic BL is an extension of MTL logic where conjunction is defined by a continuous t-norm, and implication is also defined as the residuum of the t-norm. Its models correspond to BL-algebras.
  • Łukasiewicz fuzzy logic is the extension of basic fuzzy logic BL where standard conjunction is the Łukasiewicz t-norm. It has the axioms of basic fuzzy logic plus an axiom of double negation, and its models correspond to MV-algebras.
  • Gödel fuzzy logic is the extension of basic fuzzy logic BL where conjunction is Gödel t-norm. It has the axioms of BL plus an axiom of idempotence of conjunction, and its models are called G-algebras.
  • Product fuzzy logic is the extension of basic fuzzy logic BL where conjunction is product t-norm. It has the axioms of BL plus another axiom for cancellativity of conjunction, and its models are called product algebras.
  • Fuzzy logic with evaluated syntax (sometimes also called Pavelka's logic), denoted by EVŁ, is a further generalization of mathematical fuzzy logic. While the above kinds of fuzzy logic have traditional syntax and many-valued semantics, in EVŁ is evaluated also syntax. This means that each formula has an evaluation. Axiomatization of EVŁ stems from Łukasziewicz fuzzy logic. A generalization of classical Gödel completeness theorem is provable in EVŁ.

Predicate fuzzy logics

These extend the above-mentioned fuzzy logics by adding universal and existential quantifiers in a manner similar to the way that predicate logic is created from propositional logic. The semantics of the universal (resp. existential) quantifier in t-norm fuzzy logics is the infimum (resp. supremum) of the truth degrees of the instances of the quantified subformula.

Decidability issues for fuzzy logic

The notions of a "decidable subset" and "recursively enumerable subset" are basic ones for classical mathematics and classical logic. Thus the question of a suitable extension of them to fuzzy set theory is a crucial one. A first proposal in such a direction was made by E.S. Santos by the notions of fuzzy Turing machine, Markov normal fuzzy algorithm and fuzzy program (see Santos 1970). Successively, L. Biacino and G. Gerla argued that the proposed definitions are rather questionable. For example, in [20] one shows that the fuzzy Turing machines are not adequate for fuzzy language theory since there are natural fuzzy languages intuitively computable that cannot be recognized by a fuzzy Turing Machine. Then, they proposed the following definitions. Denote by Ü the set of rational numbers in [0,1]. Then a fuzzy subset s : S [0,1] of a set S is recursively enumerable if a recursive map h : S×N Ü exists such that, for every x in S, the function h(x,n) is increasing with respect to n and s(x) = lim h(x,n). We say that s is decidable if both s and its complement –s are recursively enumerable. An extension of such a theory to the general case of the L-subsets is possible (see Gerla 2006). The proposed definitions are well related with fuzzy logic. Indeed, the following theorem holds true (provided that the deduction apparatus of the considered fuzzy logic satisfies some obvious effectiveness property).

Any "axiomatizable" fuzzy theory is recursively enumerable. In particular, the fuzzy set of logically true formulas is recursively enumerable in spite of the fact that the crisp set of valid formulas is not recursively enumerable, in general. Moreover, any axiomatizable and complete theory is decidable.

It is an open question to give supports for a "Church thesis" for fuzzy mathematics, the proposed notion of recursive enumerability for fuzzy subsets is the adequate one. In order to solve this, an extension of the notions of fuzzy grammar and fuzzy Turing machine are necessary. Another open question is to start from this notion to find an extension of Gödel's theorems to fuzzy logic.

Fuzzy databases

Once fuzzy relations are defined, it is possible to develop fuzzy relational databases. The first fuzzy relational database, FRDB, appeared in Maria Zemankova's dissertation (1983). Later, some other models arose like the Buckles-Petry model, the Prade-Testemale Model, the Umano-Fukami model or the GEFRED model by J.M. Medina, M.A. Vila et al.

Fuzzy querying languages have been defined, such as the SQLf by P. Bosc et al. and the FSQL by J. Galindo et al. These languages define some structures in order to include fuzzy aspects in the SQL statements, like fuzzy conditions, fuzzy comparators, fuzzy constants, fuzzy constraints, fuzzy thresholds, linguistic labels etc.

The knowledge graph Weaviate uses fuzzy logic to index data based on a machine learning model called the Contextionary.[21]

Comparison to probability

Fuzzy logic and probability address different forms of uncertainty. While both fuzzy logic and probability theory can represent degrees of certain kinds of subjective belief, fuzzy set theory uses the concept of fuzzy set membership, i.e., how much an observation is within a vaguely defined set, and probability theory uses the concept of subjective probability, i.e., frequency of occurrence or likelihood of some event or condition . The concept of fuzzy sets was developed in the mid-twentieth century at Berkeley [22] as a response to the lacking of probability theory for jointly modelling uncertainty and vagueness.[23]

Bart Kosko claims in Fuzziness vs. Probability[24] that probability theory is a subtheory of fuzzy logic, as questions of degrees of belief in mutually-exclusive set membership in probability theory can be represented as certain cases of non-mutually-exclusive graded membership in fuzzy theory. In that context, he also derives Bayes' theorem from the concept of fuzzy subsethood. Lotfi A. Zadeh argues that fuzzy logic is different in character from probability, and is not a replacement for it. He fuzzified probability to fuzzy probability and also generalized it to possibility theory.[25]

More generally, fuzzy logic is one of many different extensions to classical logic intended to deal with issues of uncertainty outside of the scope of classical logic, the inapplicability of probability theory in many domains, and the paradoxes of Dempster-Shafer theory.

Relation to ecorithms

Computational theorist Leslie Valiant uses the term ecorithms to describe how many less exact systems and techniques like fuzzy logic (and "less robust" logic) can be applied to learning algorithms. Valiant essentially redefines machine learning as evolutionary. In general use, ecorithms are algorithms that learn from their more complex environments (hence eco-) to generalize, approximate and simplify solution logic. Like fuzzy logic, they are methods used to overcome continuous variables or systems too complex to completely enumerate or understand discretely or exactly. [26] Ecorithms and fuzzy logic also have the common property of dealing with possibilities more than probabilities, although feedback and feed forward, basically stochastic weights, are a feature of both when dealing with, for example, dynamical systems.

Compensatory fuzzy logic

Compensatory fuzzy logic (CFL) is a branch of fuzzy logic with modified rules for conjunction and disjunction. When the truth value of one component of a conjunction or disjunction is increased or decreased, the other component is decreased or increased to compensate. This increase or decrease in truth value may be offset by the increase or decrease in another component. An offset may be blocked when certain thresholds are met. Proponents claim that CFL allows for better computational semantic behaviors and mimic natural language.[27][28]

Compensatory Fuzzy Logic consists of four continuous operators: conjunction (c); disjunction (d); fuzzy strict order (or); and negation (n). The conjunction is the geometric mean and its dual as conjunctive and disjunctive operators.[29]

IEEE STANDARD 1855–2016 – IEEE Standard for Fuzzy Markup Language

The IEEE 1855, the IEEE STANDARD 1855–2016, is about a specification language named Fuzzy Markup Language (FML)[30] developed by the IEEE Standards Association. FML allows modelling a fuzzy logic system in a human-readable and hardware independent way. FML is based on eXtensible Markup Language (XML). The designers of fuzzy systems with FML have a unified and high-level methodology for describing interoperable fuzzy systems. IEEE STANDARD 1855–2016 uses the W3C XML Schema definition language to define the syntax and semantics of the FML programs.

Prior to the introduction of FML, fuzzy logic practitioners could exchange information about their fuzzy algorithms by  adding to their software functions the ability to read, correctly parse, and store the result of their work in a  form compatible with the Fuzzy Control Language (FCL) described and specified by Part 7 of IEC 61131.[31][32]

See also


  1. Novák, V.; Perfilieva, I.; Močkoř, J. (1999). Mathematical principles of fuzzy logic. Dordrecht: Kluwer Academic. ISBN 978-0-7923-8595-0.
  2. "Fuzzy Logic". Stanford Encyclopedia of Philosophy. Bryant University. 2006-07-23. Retrieved 2008-09-30.
  3. Zadeh, L.A. (1965). "Fuzzy sets". Information and Control. 8 (3): 338–353. doi:10.1016/s0019-9958(65)90241-x.
  4. Pelletier, Francis Jeffry (2000). "Review of Metamathematics of fuzzy logics" (PDF). The Bulletin of Symbolic Logic. 6 (3): 342–346. doi:10.2307/421060. JSTOR 421060. Archived (PDF) from the original on 2016-03-03.
  5. "What is Fuzzy Logic? "Mechanical Engineering Discussion Forum"".
  6. Asli, Kaveh Hariri; Aliyev, Soltan Ali Ogli; Thomas, Sabu; Gopakumar, Deepu A. (2017-11-23). Handbook of Research for Fluid and Solid Mechanics: Theory, Simulation, and Experiment. CRC Press. ISBN 9781315341507.
  7. Chaudhuri, Arindam; Mandaviya, Krupa; Badelia, Pratixa; Ghosh, Soumya K. (2016-12-23). Optical Character Recognition Systems for Different Languages with Soft Computing. Springer. ISBN 9783319502526.
  8. Zadeh, L. A.; et al. (1996). Fuzzy Sets, Fuzzy Logic, Fuzzy Systems. World Scientific Press. ISBN 978-981-02-2421-9.
  9. Wierman, Mark J. "An Introduction to the Mathematics of Uncertainty: including Set Theory, Logic, Probability, Fuzzy Sets, Rough Sets, and Evidence Theory" (PDF). Creighton University. Archived (PDF) from the original on 30 July 2012. Retrieved 16 July 2016.
  10. Zaitsev, D.A.; Sarbei, V.G.; Sleptsov, A.I. (1998). "Synthesis of continuous-valued logic functions defined in tabular form". Cybernetics and Systems Analysis. 34 (2): 190–195. doi:10.1007/BF02742068.
  11. Kosko, B (June 1, 1994). "Fuzzy Thinking: The New Science of Fuzzy Logic". Hyperion.
  12. Bansod, Nitin A; Kulkarni, Marshall; Patil, S.H. (2005). "Soft Computing- A Fuzzy Logic Approach". In Bharati Vidyapeeth College of Engineering (ed.). Soft Computing. Allied Publishers. p. 73. ISBN 978-81-7764-632-0. Retrieved 9 November 2018.
  13. Yanase, Juri; Triantaphyllou, Evangelos (2019). "A Systematic Survey of Computer-Aided Diagnosis in Medicine: Past and Present Developments". Expert Systems with Applications. 138: 112821. doi:10.1016/j.eswa.2019.112821.
  14. Lin KP, Chang HF, Chen TL, Lu YM, and Wang CH (2016). "Intuitionistic fuzzy C-regression by using least squares support vector regression". Expert Systems with Applications: 296–304.
  15. Deng H, Deng W, Sun X, Ye C, and Zhou X (2016). "Adaptive intuitionistic fuzzy enhancement of brain tumor MR images". Scientific Reports. 6: 35760. Bibcode:2016NatSR...635760D. doi:10.1038/srep35760. PMC 5082372. PMID 27786240.
  16. Kuo RJ, Zulvia FE, Tsai CY (2018). "A hybrid metaheuristic and kernel intuitionistic fuzzy c-means algorithm for cluster analysis". Applied Soft Computing: 299–308.
  17. Vlachos IK, Sergiadis GD (2007). "Intuitionistic fuzzy information–applications to pattern recognition". Pattern Recognition Letters. 28 (2): 197–206. doi:10.1016/j.patrec.2006.07.004.
  18. Das S, Guha D, and Dutta B (2016). "Medical diagnosis with the aid of using fuzzy logic and intuitionistic fuzzy logic". Applied Intelligence. 45 (3): 850–867. doi:10.1007/s10489-016-0792-0.
  19. Yanase, Juri; Triantaphyllou, Evangelos (2019). "The Seven Key Challenges for the Future of Computer-Aided Diagnosis in Medicine". International Journal of Medical Informatics. 129: 413–422. doi:10.1016/j.ijmedinf.2019.06.017. PMID 31445285.
  20. Gerla, G. (2016). "Comments on some theories of fuzzy computation". International Journal of General Systems. 45 (4): 372–392. Bibcode:2016IJGS...45..372G. doi:10.1080/03081079.2015.1076403.
  21. Technologies, SeMI. "SeMI Technologies // Introduction". SeMI // knowledge graph services.
  22. "Lofti Zadeh Berkeley". Archived from the original on 2017-02-11.
  23. Mares, Milan (2006). "Fuzzy Sets". Scholarpedia. 1 (10): 2031. Bibcode:2006SchpJ...1.2031M. doi:10.4249/scholarpedia.2031.
  24. Kosko, Bart. "Fuzziness vs. Probability" (PDF). University of South California. Retrieved 9 November 2018.
  25. Novák, V (2005). "Are fuzzy sets a reasonable tool for modeling vague phenomena?". Fuzzy Sets and Systems. 156 (3): 341–348. doi:10.1016/j.fss.2005.05.029.
  26. Valiant, Leslie (2013). Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World. New York: Basic Books. ISBN 978-0465032716.
  27. "Archived copy" (PDF). Archived (PDF) from the original on 2015-10-04. Retrieved 2015-10-02.CS1 maint: archived copy as title (link)
  28. Veri, Francesco (2017). "Fuzzy Multiple Attribute Conditions in fsQCA: Problems and Solutions". Sociological Methods & Research: 004912411772969. doi:10.1177/0049124117729693.
  29. Cejas, Jesús (2011). "Compensatory Fuzzy Logic". Revista de Ingeniería Industrial. ISSN 1815-5936.
  30. Acampora, Giovanni; Di Stefano, Bruno N.; Vitiello, Autilia (2016). "IEEE 1855™: The First IEEE Standard Sponsored by IEEE Computational Intelligence Society [Society Briefs]". IEEE Computational Intelligence Magazine. 11 (4): 4–6. doi:10.1109/MCI.2016.2602068.
  31. Di Stefano, Bruno N. (2013). "On the Need of a Standard Language for Designing Fuzzy Systems". On the Power of Fuzzy Markup Language. Studies in Fuzziness and Soft Computing. 296. pp. 3–15. doi:10.1007/978-3-642-35488-5_1. ISBN 978-3-642-35487-8. ISSN 1434-9922.
  32. Acampora, Giovanni; Loia, Vincenzo; Lee, Chang-Shing; Wang, Mei-Hui (2013). On the Power of Fuzzy Markup Language. Studies in Fuzziness and Soft Computing. 296. doi:10.1007/978-3-642-35488-5. ISBN 978-3-642-35487-8. ISSN 1434-9922.


  • Arabacioglu, B. C. (2010). "Using fuzzy inference system for architectural space analysis". Applied Soft Computing. 10 (3): 926–937. doi:10.1016/j.asoc.2009.10.011.
  • Biacino, L.; Gerla, G. (2002). "Fuzzy logic, continuity and effectiveness". Archive for Mathematical Logic. 41 (7): 643–667. CiteSeerX doi:10.1007/s001530100128. ISSN 0933-5846.
  • Cox, Earl (1994). The fuzzy systems handbook: a practitioner's guide to building, using, maintaining fuzzy systems. Boston: AP Professional. ISBN 978-0-12-194270-0.
  • Gerla, Giangiacomo (2006). "Effectiveness and Multivalued Logics". Journal of Symbolic Logic. 71 (1): 137–162. doi:10.2178/jsl/1140641166. ISSN 0022-4812.
  • Hájek, Petr (1998). Metamathematics of fuzzy logic. Dordrecht: Kluwer. ISBN 978-0-7923-5238-9.
  • Hájek, Petr (1995). "Fuzzy logic and arithmetical hierarchy". Fuzzy Sets and Systems. 3 (8): 359–363. doi:10.1016/0165-0114(94)00299-M. ISSN 0165-0114.
  • Halpern, Joseph Y. (2003). Reasoning about uncertainty. Cambridge, Massachusetts: MIT Press. ISBN 978-0-262-08320-1.
  • Höppner, Frank; Klawonn, F.; Kruse, R.; Runkler, T. (1999). Fuzzy cluster analysis: methods for classification, data analysis and image recognition. New York: John Wiley. ISBN 978-0-471-98864-9.
  • Ibrahim, Ahmad M. (1997). Introduction to Applied Fuzzy Electronics. Englewood Cliffs, NJ: Prentice Hall. ISBN 978-0-13-206400-2.
  • Klir, George Jiří; Folger, Tina A. (1988). Fuzzy sets, uncertainty, and information. Englewood Cliffs, NJ: Prentice Hall. ISBN 978-0-13-345984-5.
  • Klir, George Jiří; St. Clair, Ute H.; Yuan, Bo (1997). Fuzzy set theory: foundations and applications. Englewood Cliffs, NJ: Prentice Hall. ISBN 978-0-13-341058-7.
  • Klir, George Jiří; Yuan, Bo (1995). Fuzzy sets and fuzzy logic: theory and applications. Upper Saddle River, NJ: Prentice Hall PTR. ISBN 978-0-13-101171-7.
  • Kosko, Bart (1993). Fuzzy thinking: the new science of fuzzy logic. New York: Hyperion. ISBN 978-0-7868-8021-8.
  • Kosko, Bart; Isaka, Satoru (July 1993). "Fuzzy Logic". Scientific American. 269 (1): 76–81. Bibcode:1993SciAm.269a..76K. doi:10.1038/scientificamerican0793-76.
  • Lohani, A. K.; Goel, N. K.; Bhatia, K. K. S. (2006). "Takagi–Sugeno fuzzy inference system for modeling stage–discharge relationship". Journal of Hydrology. 331 (1): 146–160. Bibcode:2006JHyd..331..146L. doi:10.1016/j.jhydrol.2006.05.007.
  • Lohani, A. K.; Goel, N. K.; Bhatia, K. K. S. (2007). "Deriving stage–discharge–sediment concentration relationships using fuzzy logic". Hydrological Sciences Journal. 52 (4): 793–807. doi:10.1623/hysj.52.4.793.
  • Lohani, A. K.; Goel, N. K.; Bhatia, K. K. S. (2011). "Comparative study of neural network, fuzzy logic and linear transfer function techniques in daily rainfall‐runoff modelling under different input domains". Hydrological Processes. 25 (2): 175–193. Bibcode:2011HyPr...25..175L. doi:10.1002/hyp.7831.
  • Lohani, A. K.; Goel, N. K.; Bhatia, K. K. S. (2012). "Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques". Journal of Hydrology. 442–443 (6): 23–35. Bibcode:2012JHyd..442...23L. doi:10.1016/j.jhydrol.2012.03.031.
  • Moghaddam, M. J.; Soleymani, M. R.; Farsi, M. A. (2013). "Sequence planning for stamping operations in progressive dies". Journal of Intelligent Manufacturing: 1–11.
  • Masmoudi, Malek; Haït, Alain (July 2012). Project scheduling under uncertainty using fuzzy modeling and solving techniques, Engineering Applications of Artificial Intelligence. Elsevier.
  • Masmoudi, Malek; Haït, Alain (November 2012). "Fuzzy uncertainty modelling for project planning; application to helicopter maintenance" (PDF). International Journal of Production Research. 50 (24).
  • Merigo, Jose M.; Gil-Lafuente, Anna M.; Yager, Ronald R. (2015). "An overview of fuzzy research with bibliometric indicators". Applied Soft Computing. 27: 420–433. doi:10.1016/j.asoc.2014.10.035. ISSN 1568-4946.
  • Mironov, A. (2005). "Fuzzy modal logic". Journal of Mathematical Sciences. 128 (6): 3461–3483. doi:10.1007/s10958-005-0281-1. ISSN 1072-3374.
  • Montagna, F. (2001). "Three complexity problems in quantified fuzzy logic". Studia Logica. 68 (1): 143–152. doi:10.1023/A:1011958407631. ISSN 0039-3215.
  • Mundici, Daniele; Cignoli, Roberto; D'Ottaviano, Itala M. L. (1999). Algebraic foundations of many-valued reasoning. Dordrecht: Kluwer Academic. ISBN 978-0-7923-6009-4.
  • Novák, Vilém (1989). Fuzzy Sets and Their Applications. Bristol: Adam Hilger. ISBN 978-0-85274-583-0.
  • Novák, Vilém (2005). "On fuzzy type theory". Fuzzy Sets and Systems. 149 (2): 235–273. doi:10.1016/j.fss.2004.03.027.
  • Novák, Vilém; Perfilieva, Irina; Močkoř, Jiří (1999). Mathematical principles of fuzzy logic. Dordrecht: Kluwer Academic. ISBN 978-0-7923-8595-0.
  • Onses, Richard (1996). Second Order Experton: A new Tool for Changing Paradigms in Country Risk Calculation. ISBN 978-84-7719-558-0.
  • Onses, Richard (1994). Détermination de l´incertitude inhérente aux investissements en Amérique Latine sur la base de la théorie des sous ensembles flous. Barcelona. ISBN 978-84-475-0881-5.
  • Passino, Kevin M.; Yurkovich, Stephen (1998). Fuzzy control. Boston: Addison-Wesley. ISBN 978-0-201-18074-9.
  • Pedrycz, Witold; Gomide, Fernando (2007). Fuzzy systems engineering: Toward Human-Centerd Computing. Hoboken: Wiley-Interscience. ISBN 978-0-471-78857-7.
  • Pu, Pao Ming; Liu, Ying Ming (1980). "Fuzzy topology. I. Neighborhood structure of a fuzzy point and Moore-Smith convergence". Journal of Mathematical Analysis and Applications. 76 (2): 571–599. doi:10.1016/0022-247X(80)90048-7. ISSN 0022-247X.
  • Sahoo, Bhabagrahi; Lohani, A. K.; Sahu, Rohit K. (2006). "Fuzzy multiobjective and linear programming based management models for optimal land-water-crop system planning". Water Resources Management, Springer Netherlands. 20 (6): 931–948. doi:10.1007/s11269-005-9015-x.
  • Santos, Eugene S. (1970). "Fuzzy Algorithms". Information and Control. 17 (4): 326–339. doi:10.1016/S0019-9958(70)80032-8.
  • Scarpellini, Bruno (1962). "Die Nichaxiomatisierbarkeit des unendlichwertigen Prädikatenkalküls von Łukasiewicz". Journal of Symbolic Logic. 27 (2): 159–170. doi:10.2307/2964111. ISSN 0022-4812. JSTOR 2964111.
  • Seising, Rudolf (2007). The Fuzzification of Systems. The Genesis of Fuzzy Set Theory and Its Initial Applications -- Developments up to the 1970s. Springer-Verlag. ISBN 978-3-540-71795-9.
  • Steeb, Willi-Hans (2008). The Nonlinear Workbook: Chaos, Fractals, Cellular Automata, Neural Networks, Genetic Algorithms, Gene Expression Programming, Support Vector Machine, Wavelets, Hidden Markov Models, Fuzzy Logic with C++, Java and SymbolicC++ Programs (4 ed.). World Scientific. ISBN 978-981-281-852-2.
  • Tsitolovsky, Lev; Sandler, Uziel (2008). Neural Cell Behavior and Fuzzy Logic. Springer. ISBN 978-0-387-09542-4.
  • Wiedermann, J. (2004). "Characterizing the super-Turing computing power and efficiency of classical fuzzy Turing machines". Theoretical Computer Science. 317 (1–3): 61–69. doi:10.1016/j.tcs.2003.12.004.
  • Yager, Ronald R.; Filev, Dimitar P. (1994). Essentials of fuzzy modeling and control. New York: Wiley. ISBN 978-0-471-01761-5.
  • Van Pelt, Miles (2008). Fuzzy Logic Applied to Daily Life. Seattle, WA: No No No No Press. ISBN 978-0-252-16341-8.
  • Von Altrock, Constantin (1995). Fuzzy logic and NeuroFuzzy applications explained. Upper Saddle River, NJ: Prentice Hall PTR. ISBN 978-0-13-368465-0.
  • Wilkinson, R. H. (1963). "A method of generating functions of several variables using analog diode logic". IEEE Transactions on Electronic Computers. 12 (2): 112–129. doi:10.1109/PGEC.1963.263419.
  • Zadeh, L. A. (1968). "Fuzzy algorithms". Information and Control. 12 (2): 94–102. doi:10.1016/S0019-9958(68)90211-8. ISSN 0019-9958.
  • Zadeh, L. A. (1965). "Fuzzy sets". Information and Control. 8 (3): 338–353. doi:10.1016/S0019-9958(65)90241-X. ISSN 0019-9958.
  • Zaitsev, D. A.; Sarbei, V. G.; Sleptsov, A. I. (1998). "Synthesis of continuous-valued logic functions defined in tabular form". Cybernetics and Systems Analysis. 34 (2): 190–195. doi:10.1007/BF02742068.
  • Zemankova-Leech, M. (1983). "Fuzzy Relational Data Bases". Ph. D. Dissertation. Florida State University. Cite journal requires |journal= (help)
  • Zimmermann, H. (2001). Fuzzy set theory and its applications. Boston: Kluwer Academic Publishers. ISBN 978-0-7923-7435-0.
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