Penentuan Keputusan Multi Kriteria Dengan Himpunan Samar

Decision support systems to solve multi-criteria problems can use fuzzy set approach. Fuzzy set operations used in this article are the average operating vague (arithmetic mean) and slices of vague, like a fuzzy standard, Einstein product and algebraic product. The results showed that the ability to explain the criteria of incision surgery better than the arithmetic mean operation

Quasi-arithmetic means and OWA functions in interval-valued and Atanassov's intuitionistic fuzzy set theory

In this paper we propose an extension of the well-known OWA functions introduced by Yager to interval-valued (IVFS) and Atanassov’s intuitionistic (AIFS) fuzzy set theory. We first extend the arithmetic and the quasi-arithmetic mean using the arithmetic operators in IVFS and AIFS theory and investigate under which conditions these means are idempotent. Since on the unit interval the construction of the OWA function involves reordering the input values, we propose a way of transforming the input values in IVFS and AIFS theory to a new list of input values which are now ordered

FUZZY ROBUST ESTIMATES OF LOCATION AND SCALE PARAMETERS OF A FUZZY RANDOM VARIABLE

A random variable is a variable whose components are random values. To characterise a random variable, the arithmetic mean is widely used as an estimate of the location parameter, and variation as an estimate of the scale parameter. The disadvantage of the arithmetic mean is that it is sensitive to extreme values, outliers in the data. Due to that, to characterise random variables, robust estimates of the location and scale parameters are widely used: the median and median absolute deviation from the median. In real situations, the components of a random variable cannot always be estimated in a deterministic way. One way to model the initial data uncertainty is to use fuzzy estimates of the components of a random variable. Such variables are called fuzzy random variables. In this paper, we examine fuzzy robust estimates of location and scale parameters of a fuzzy random variable: fuzzy median and fuzzy median of the deviations of fuzzy component values from the fuzzy median.

On extending generalized Bonferroni means to Atanassov orthopairs in decision making contexts

Extensions of aggregation functions to Atanassov orthopairs (often referred to as intuitionistic fuzzy sets or AIFS) usually involve replacing the standard arithmetic operations with those defined for the membership and non-membership orthopairs. One problem with such constructions is that the usual choice of operations has led to formulas which do not generalize the aggregation of ordinary fuzzy sets (where the membership and non-membership values add to 1). Previous extensions of the weighted arithmetic mean and ordered weighted averaging operator also have the absorbent element 〈1,0〉, which becomes particularly problematic in the case of the Bonferroni mean, whose generalizations are useful for modeling mandatory requirements. As well as considering the consistency and interpretability of the operations used for their construction, we hold that it is also important for aggregation functions over higher order fuzzy sets to exhibit analogous behavior to their standard definitions. After highlighting the main drawbacks of existing Bonferroni means defined for Atanassov orthopairs and interval data, we present two alternative methods for extending the generalized Bonferroni mean. Both lead to functions with properties more consistent with the original Bonferroni mean, and which coincide in the case of ordinary fuzzy values.<br /

Characterization of unidimensional averaged similarities

A T-indistinguishability operator (or fuzzy similarity relation) E is called unidimensional when it may be obtained from one single fuzzy subset (or fuzzy criterion). In this paper, we study when a T-indistinguishability operator that has been obtained as an average of many unidimensional ones is unidimensional too. In this case, the single fuzzy subset used to generate E is explicitly obtained as the quasi-arithmetic mean of all the fuzzy criteria primarily involved in the construction of E.Peer ReviewedPostprint (author's final draft

SUB-TRIDENT FORM USING FUZZY AGGREGATION

This Paper deals with the solution to find the Optimal Path and the Optimal Solution with the help of Fuzzy Aggregation Operations such as Arithmetic Mean and Geometric Mean and by using Trapezoidal Fuzzy Numbers through Pascal’s Triangle Graded Mean Approach. Here the results are obtained as Fuzzy Sub-Triangular Form and this form in turn converted to Sub-Triangular Form. The Minimum value of Sub-Trident Form gives the Shortest Path and the Optimum Solution is obtained by giving a suitable numerical example

PENENTUAN KEPUTUSAN MULTI KRITERIA DENGAN HIMPUNAN SAMAR

Decision support systems to solve multi-criteria problems can use fuzzy set approach. Fuzzy set operations used in this article are the average operating vague (arithmetic mean) and slices of vague, like a fuzzy standard, Einstein product and algebraic product. The results showed that the ability to explain the criteria of incision surgery better than the arithmetic mean operation.   Keywords: rerata aritmetika, standard fuzzy, Einstein product, algebraic product

ET-Lipschitzian aggregation operators

Lipschitzian and kernel aggregation operators with respect to the natural Tindistinguishability operator ET and their powers are studied. A t-norm T is proved to be ET -lipschitzian, and is interpreted as a fuzzy point and a fuzzy map as well. Given an archimedean t-norm T with additive generator t, the quasi-arithmetic mean generated by t is proved to be the more stable aggregation operator with respect to T.Peer ReviewedPostprint (published version

Problems of Choquet Integral Practical Applications

Choquet integral with respect to fuzzy measure is a generalization of weighted arithmetic mean aggregation operator. It allows taking into account the phenomenon of dependence between criteria. Due to this it is possible to reflect the expert knowledge more accurately without making the model simplification which is the assumption of independence of the aggregation criteria. The problems of Choquet fuzzy integral applications and possible ways of overcoming them are discussed. Practical applications for this relatively new apparatus are reviewed

Aggregation operators and lipschitzian conditions

Lipschitzian aggregation operators with respect to the natural T - indistin- guishability operator Et and their powers, and with respect to the residuation ! T with respect to a t-norm T and its powers are studied. A t-norm T is proved to be E T -Lipschitzian and -Lipschitzian, and is interpreted as a fuzzy point and a fuzzy map as well. Given an Archimedean t-norm T with additive generator t , the quasi- arithmetic mean generated by t is proved to be the most stable aggregation operator with respect to TPeer Reviewe