Fuzzy Set Theory

Fuzzy Set Theory

  • Sudipta Midya added an answer:
    What are the difference between Rough Set, Near Set, Shadow Set and Fuzzy Sets? With Example?

    What are difference between Rough Set, Near Set, Shadow Set and Fuzzy Sets? With Example?

    Sudipta Midya

    Rough set and Fuzzy set are different types of uncertainty. Rough set is more flexible and occupy more data than fuzzy set. Also, optimal solution extracted from an optimization problem  rough data is better than fuzzy data.   

  • Ines Bayoudh Saâdi added an answer:
    Does anyone have a good reference on fuzzy Case based reasoning ?

    Does any one have a good reference on fuzzy Case based reasoning with an illustrative example ?

    Ines Bayoudh Saâdi

    Thank you Midya for the selected Papers

    Best Regards

  • Harish Garg added an answer:
    Sum of memebership functions for a special X value?

    As you know membership functions can overlap, which means a value of X can belong to more than one fuzzy set.

    The attached image is my membership functions for a variable.

    My question is that should the sum of membership values for a special X, be equal to 1?

    As you see in the image sum of two memberships at X=30 isn't equal to 1.

    what is wrong with this variable fuzzification?

    Thanks for your consideration.

    Harish Garg

    I think, the reason is the variables POOR and VERY POOR in your figure are not of same nature. One is increasing membership function while other has decreasing so you cant rule the theory that sum of them is less than 1. Perhaps all the other variables have increasing membership function.  So i think you should firstly normalizing it like taking the compliment of membership function of VERY POOR  (one minus the membership function) then you will see that their corresponding normalizing membership function sum is less than one.

  • Omar Bouattane added an answer:
    How can I tranform crisp data into intutionistic fuzzy sets (IFS)?

    Does anybody can suggest me how to convert crisp data into intuitionistic fuzzy sets (IFS)?

     For example I have quantitative data about CO2, energy consumption, noise etc. How can transform these data into μ (x) and ν (x)?

    Thank in advance for help.

    Omar Bouattane

    Fuzzy rules in the fuzzy set part of matlab toolbox is the very instructive document to understand fuzzy data  concepts;

    I agree with Pr. Mahmoud;

  • B.K. Tripathy added an answer:
    What is the meaning when it is said that fuzzy sets and rough sets lack sufficient parameters?

    In his first paper on Soft sets, Molodtsov has pointed out that the lack of parameters in fuzzy set theory and rough set theory has led to the development of Soft sets.

    But what is meant by this statement of his? We use parameters in both these types of sets as sell. In fact his example on houses can be represented as an information system used in rough set theory. Rough set theory is very comfortable in dealing with categorical attributes like "expensive", "beautiful", "green surroundings".

    Can some one elaborate on this! I am not expecting but would be very much pleased to get one from Molodtsov himself.

    B.K. Tripathy

    The question needs to be modified. After verification and correspondence with Prof.Molodtsov i found that it is lack of parametrization in fuzzy set theory which is instrumental in the introduction of soft sets. Also, as informed by him topology has similarity to the soft set concept. This point will be further clarified later.

  • Donald Myers added an answer:
    Has anyone investigated the aleatory uncertainty of Kriging?

    Generally, two different uncertainty sources, including aleatory uncertainty and epistemic uncertainty are studied. There are some different methods for exploring epistemic uncertainty, including random theory, fuzzy set theory, etc. Then how and what method would be suitable for revealing aleatory uncertainty when comparing two or more different models(e.g. spatial distribution mapping/predictive mapping of soils such as kriging and artificial neural networks)?

    Donald Myers

    1988 Warrick, A.W., Zhang, R., El-Haris, M.K. and Myers, D.E., Direct comparisons between kriging and other interpolators in Proceedings of the Validation of Flow and Transport Models in the Unsaturated Zone, Ruidoso, NM, 23-26 May, 1988, 505-510

    If you don't find it elsewhere then go to my homepage, look at the link for "papers" and scroll down to this paper, right click and choose "save link as"

    My point above is that there are various sources of uncertainty, some pertain to choosing the interpolation method, some to choices once the methodology has been chosen, some due to whether the method is based on theoretical assumptions and whether these are satisfied, there may even be uncertainties associated with the data (not only measurement/observation/analytical but spatial location uncertainties. I suggest that in most cases the real question is whether the results are useful and make sense. For more philosophical discussions I refer you to Matheron's "Estimating and Choosing", also see the last part of his 1971 notes on "The theory of regionalized variables" or perhaps "Geostatistics" by J.-P. Chiles and P. Delfiner (J. Wiley), both were early students of Matheron's

  • Behrouz Ahmadi-Nedushan added an answer:
    How to differentiate rough sets and fuzzy sets?
    Y.Y.Yao has written a paper on the topic of the question.
  • B.K. Tripathy added an answer:
    How can soft set theory be applied in game theory?

    reference to a paper is given by Molodtsov in his paper, "Soft set theory first results". But the referred paper is in Russian.

    B.K. Tripathy

    I could translate one of the papers sent by Dmitry to English partially. Other papers are yet to reach me.

  • Debasish Majumder added an answer:
    Anyone familiar with fuzzy membership functions?

    Is there any restrictions in the number of membership functions of FLC?

    Is it must to have all the output MFs  related with rule base if we have more than what we need?

    Say I have 2 input variables with 4 subsets each which results in 16 rules. I have selected 40 membership functions in the output with very small values and amongst them only 12 are related to my rule base. Other 28 M.Fs lies between the range.. Can I implement a FLC like that? 

    Debasish Majumder

    Yes, there is no restriction regarding the no. of MFs. But the case that you mentioned is not a good FLC, because when we design a FLC, we consider only those MFs for output parameter which we desire for our context.  Now, if any MF of output parameter is absent in the consequent part of fuzzy if-then rules throughout the whole rule-base, then it implies that in our context, there will be no such  situation where I have to correspond our output to that particular MF. Thus,  there is no need for that particular MF to consider for our output parameter at all.

  • Konstantinos Dragonas added an answer:
    Regarding this abstract, is there a system with failure interaction so that a failure of component impacts on the other component failure?

    Repairable system is commonly used in different industries and has been become more complex. In complex system, in addition to the random component failure, the effect of two component is random. The impact of failure is uncertain and can be considered as fuzzy. Fuzzy set theory has been the most important approach used to deal with uncertainty in problems. The repair of complex system divided to soft and hard. The hard failure causes the system stop and the soft failure does not, but it increases the system operating costs too. In this paper assumed: when the first component fails, it remains in a failed state until the next inspection time. Therefore, if the first component failed in each inspection interval, a downtime penalty cost is incurred. The cost is proportional to the elapsed time from failure time to its detection at inspection time. The short inspection interval increase the number of inspection and cause the extra cost for system. As well, long inspection interval will cause the greater cost due to long elapsed between real occurrence of the failure time and the failure detection (penalty cost). On a finite time horizon, the objective is to find the optimal inspection interval for the soft failure component so that the expected total cost will be minimized.

    Konstantinos Dragonas

    Continues Time Markov Chain is used in this paper in order to optimize the Systems Test Interval and the frequency the system being tested in order to maximize the availability of the System.


  • Xiaodong Pan added an answer:
    What are the areas in which IF set theory has proved its utility so far?

    Intuitionistic Fuzzy Set Theory

    Xiaodong Pan

    It's a very intresting question, this question seemingly also exists in other fields, such as fuzzy sets, soft sets, rough sets etc. Maybe you will argue that IFS, FS, Soft sets can be used to solve many practical problems, and there are already many such papers in these fields. But we should notice that as a kind of mathematical tool,  IFS, FS, Soft sets are not exclusive in these applications; in other words, usually it's okay to substitue other methods for IFS, FS, Soft sets. This is very different from traditional maths.

  • Fehmi Burcin Ozsoydan added an answer:
    How do I choose membership functions in a fuzzy system?
    Membership functions (MFs) are the building blocks of fuzzy set theory, i.e., fuzziness in a fuzzy set is determined by its MF. Accordingly, the shapes of MFs are important for a particular problem since they effect on a fuzzy inference system. They may have different shapes like triangular, trapezoidal, Gaussian, etc. The only condition a MF must really satisfy is that it must vary between 0 and 1. What are some other criterion that I need to be aware of to make a sensible choice of the MF?
    Fehmi Burcin Ozsoydan

    You can use related matlab toolbox but first you need data collected from real-life system to which you will apply fuzzy. You can try various fuzzy functions from among triangular or other forms and make decision about which reflects the case better. Generally speaking triangular is one of most encountered one in practice. Good luck..

  • Ravindra Ramachandra Mudholkar added an answer:
    How can I choose membership function in fuzzy systems?

    Im working on fuzzy FMEA(Failure mode and effects analysis)  project. i cant choose membership function for O,D,S and RPN. which membership function do you suggest for such a project?

    plz help me

  • Bajece Balkan Journal of Electrical added an answer:
    What are the limitations of Grey set as uncertainty model?

    Grey theory is an extension of fuzzy set theory and rough set theory where two memberships function a lower one and an upper one are used with interval. Grey system theory is a unique concept which deals with continuous systems including uncertain. Grey theory classifies sets into three groups: White sets, Black sets and Grey sets. White set contains objects that have complete knowledge behavior, while black set contains objects which have unknown behavior.
    What are the limitation of Grey set as uncertainty model?

  • Nouran Radwan added an answer:
    What is the difference between type1 - fuzzy logic and type 2 - fuzzy logic ?
    Nouran Radwan

    In Type 1 fuzzy set , Expert should determine the degree of achieving the characteristics of the object. For example, if you have a 3 different red balls. The first is red by 75%, second is red 85%, Third is red 95%. 
    In Type 2 Fuzzy set, Expert can't determine exactly the degree of achieving the characteristics. For example, if you have a 3 different red balls. The first is red by 75%-80%, second is red 85%-90%, Third is red 95%-100%. So it presents an interval fuzzy set.

  • Sudev Naduvath added an answer:
    Which is the best book to study and get solutions over maths related to Fuzzy Logic for beginners?
    I am a beginner and am studying fuzzy logic from the book "Fuzzy sets and Fuzzy logic" by M. Ganesh, now there's a problem with the maths section, actually I find maths too difficult to understand from that book so would like to know some other book and prepare for that.
    Sudev Naduvath

    Please see the following links:







  • B.K. Tripathy added an answer:
    How true is it that fuzzy sets and rough sets are soft sets?

    In his first paper on soft sets Molodtsov (1999) has argued that all fuzzy sets are soft sets and in 2010 Herawan has written a paper to show that rough sets are also soft sets.

    B.K. Tripathy

    For deriving any answer one has to refer the following two papers:

    D. Molodtsov, Soft set theory-First results, Computers and Mathematics with Applications, 37, (1999), pp.19-31

    T.Herawan and M.deris, A direct proof of every rough set is a soft set, 3rd Asia international conference on modelling and simulation, (2009), pp.119 - 124.

  • Sarfaraz Hashemkhani Zolfani added an answer:
    What are the basics of fuzzy Mic-Mac analysis (ISM Modelling)?

    Researchers in Decision modelling and Fuzzy Logic

    Sarfaraz Hashemkhani Zolfani

    Dear friend

    As far as I may know, Mic Mac using for Cross Impact analysis and also related to futures studies. 

  • Sudev Naduvath added an answer:
    Fuzzy set - what is meant by level set?
    I am a new student in Fuzzy sets & Fuzzy relations. I can found that the Level of a fuzzy set as
    ΛA = {α/μA(x) = α for some x belongs to X} , Please provide an example of this with a set.
    Sudev Naduvath

    Please see the following articles.

    1. http://www.mv.helsinki.fi/home/niskanen/zimmermann_review.pdf

    2. http://www2.cs.uregina.ca/~yyao/PAPERS/combination.pdf

    3. sc.nahrainuniv.edu.iq/download.php?syllabus=MATH%20317.pdf

    4. debian.fmi.uni-sofia.bg/~cathy/SoftCpu/fuzzy_geometry.pdf

    5. www.new1.dli.ernet.in/data1/upload/insa/INSA_1/2000617d_565.pdf

    Hope that these references will be useful to you...

  • Ganesan G added an answer:
    What is the best way for choosing membership function in fuzzy logic?

    In order to obtain batter results in ANFIS, different membership functions are used. Is there any inductive way for obtaining best membership function based on the type of data used?

    Ganesan G

    It is always better using testing of hypothesis through samples to fix membership values.

  • Dragan Pamučar added an answer:
    Please, help me with type-reduction of interval type-2 fuzzy sets?

    I have a problem with Karnik-Mendel algorithm - computing yl, yr and switch points. Please, give me example how to compute yl, yr and switch points.

    Dragan Pamučar

    Thanks, this is helpful.

  • Robert Lowen added an answer:
    Is the definition of Dubois to be corrected regarding Rough-Fuzzy Sets?

    In Dubois Rough-fuzzy Model, Min and Max operators are used for Lower and Upper Approximations. Can we swap Min and Max operators?

  • Morteza Seidi added an answer:
    What is the best approach for generation of fuzzy rule ?
    Morteza Seidi

    Hi Mohammad

    In case you have the model of the system, by using model-based fuzzy control systems such as Takagi-Sugeno, you can obtain fuzzy rules and membership functions analytically and guarantee the closed loop stability. The attached is a chapter on Takagi-Sugeno fuzzy model.

    Good luck

  • Nasruddin Hassan added an answer:
    How can I define an numerical example (specially real world problem) for temporal intuitionistic fuzzy sets.?

    There are several studies about Atanassov Temporal Intuitionistic Fuzzy Sets. But I can not find an numerical example for it. (Specially real world problem.)

    Nasruddin Hassan

    Look up articles written by Shawkat Alkhazaleh, Khaleed Alhazaymeh and Abdul Razak Salleh amongst others

  • Angel Garrido added an answer:
    What is the difference between Fuzzy Gray and Grey Fuzzy?

    Can anybody tell me that more precisely, what is a "Fuzzy grey Set"? what is a " grey fuzzy Set"?  And what is the difference between “Fuzzy ”, “grey” and “Rough Set?” please ?

    What is the difference between Fuzzy Gray and GreyFuzzy?

  • Lawrence Ibeh added an answer:
    What is the method of constructing the membership function of a fuzzy vector?
    I think in the mathematics of fuzziness the membership function of a fuzzy vector is heuristically assumed. Is there any logical method to construct the membership function of a fuzzy vector?
    Lawrence Ibeh

    I used  the  matlab fuzzy logic tool box,  after plotting a membership function how  do I derive  my membership values automatically to be used for  a further analysis? Can any one help?

  • Ganesan G added an answer:
    What are the advantages and disadvantages of using fuzzy with rough set?
    A rough membership function may be interpreted as a special kind of fuzzy membership function. Under this interpretation, is it possible to re-express the standard rough set approximations, and to establish their connection to the core and support of a fuzzy set
    but what is the weakness and strengths of this model?
    Ganesan G

    I don't find any disadvantage. The advantage is expanding rough approximations into fuzzy environment which help to obtain solutions for various real time problems [since a good number of real time problems are fuzzy in nature]

  • Closed account added an answer:
    Airborne Lidar and Hyperspectral data fusion: which software to use?

    I have several airborne hyperspectral 65-105 band datasets with simultaneously acquired lidar data and RGB orthophotos too. Each source has usually a different GSD. I don't have any experience with data fusion so far. My task is to evaluate currently available tools and methods e.g. algebraic procedures, Dempster-Shafer Theory, neural networks, Bayesian networks, fuzzy set theory, principle component analysis or any combined methodology. Suggestions for particular method and tool are welcome.

    I have access to commercial (ENVI5, ArcGIS10.2, eCognition8) and free software (QGIS, GRASS, FUSION). Maybe you know of something better for this task..

    Thanks !


    I have now access to ERDAS Imagine 2014 too.. maybe helps.. It's a shame I don't have latest ENVI 5.2 with LIDAR module..

  • Danilo Rastovic added an answer:
    What is the most significant success of the fuzzy sets theory?
    What is the greatest achievement of fuzzy theory in the all applications area and scientific work?
    Danilo Rastovic

    Words have also ability of bifurcations as it is in some cases at equations ( linear or nonlinear) . Then people must use quasi-regularization for next treatment.

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