Research Article  Open Access
A. Rahmani, F. Hosseinzadeh Lotfi, M. RostamyMalkhalifeh, T. Allahviranloo, "A New Method for Defuzzification and Ranking of Fuzzy Numbers Based on the Statistical Beta Distribution", Advances in Fuzzy Systems, vol. 2016, Article ID 6945184, 8 pages, 2016. https://doi.org/10.1155/2016/6945184
A New Method for Defuzzification and Ranking of Fuzzy Numbers Based on the Statistical Beta Distribution
Abstract
Granular computing is an emerging computing theory and paradigm that deals with the processing of information granules, which are defined as a number of information entities grouped together due to their similarity, physical adjacency, or indistinguishability. In most aspects of human reasoning, these granules have an uncertain formation, so the concept of granularity of fuzzy information could be of special interest for the applications where fuzzy sets must be converted to crisp sets to avoid uncertainty. This paper proposes a novel method of defuzzification based on the mean value of statistical Beta distribution and an algorithm for ranking fuzzy numbers based on the crisp number ranking system on R. The proposed method is quite easy to use, but the main reason for following this approach is the equality of left spread, right spread, and mode of Beta distribution with their corresponding values in fuzzy numbers within interval, in addition to the fact that the resulting method can satisfy all reasonable properties of fuzzy quantity ordering defined by Wang et al. The algorithm is illustrated through several numerical examples and it is then compared with some of the other methods provided by literature.
1. Introduction
Granular computing is an emerging computing paradigm that is concerned with the processing of information entities created from the process of data abstraction and is intrinsically linked with the adjustable nature of human perception [1, 2]. Information granularity and granular computing have been widely used in development of verbal and linguistic concepts and particularly those concerned with fuzzy and rough sets [3] and are valuable assets for creating realistic models for realworld decisionmaking processes, as they provide the means to understand and solve abstract problems of the real world with simplicity, clarity, good approximation, and tolerance of uncertainty [1, 4]. Granular computing is the science of building heterogeneous and multilevel models for the processing of granular information by incorporating distinct concepts such as probabilistic sets, rough sets, and especially fuzzy sets and their membership functions into a single framework, thereby allowing the verbal, linguistic, and humancentered concepts to be processed.
Since the introduction of the term “granular computing,” its related concepts have appeared in many different fields such as artificial intelligence, decisionmaking, and cluster analysis [5–7]. Although there have been some works in regard with granular models, granular computing is yet to be fully and exclusively explored, and its current structures and especially those related to fuzzy sets seem to be underdeveloped.
In 1997, Zadeh [8] introduced a strong relationship between granular data and fuzzy sets and developed the theory of fuzzy information granulation to provide a new angle of approach for tackling the problem of ambiguity and uncertainty. The theory of fuzzy information granulation (TFIG) is an informal method of using linguistic variables and fuzzy IFTHEN rules to make rational decisions in an environment full of uncertainty. Thus, this article is focused on this aspect of fuzzy theory and provides a method for defuzzification of fuzzy sets to achieve certainty in solution of realworld problems.
The concept of fuzzy sets (referring to the sets with imprecise and ambiguous nature) was first introduced in 1965 by Zadeh [9]. He expanded the notion of membership beyond the “zeroone” logic and utilized the dynamic infinite space between these values. In the mid1980s, Japanese industrialists used this worthwhile concept to develop a scheme of fully automated subway controls, which demonstrated its realworld application encouraged a whole new wave of researchers to study its theoretical and practical potentials.
The foundation of conventional mathematics is based on real numbers and the process of defuzzifying and ranking fuzzy quantities—such as color or quality of goods—plays a significant role in data analysis, economics, and industrial systems, so an extensive amount of research has been dedicated to this specific subject.
Following the previous works [10–25], this study proposes a novel method for defuzzifying and ranking fuzzy numbers by using the mean value of Beta distribution. Some of the previous works in the literature are as follows.
In 1980, Yager [19] proposed a method for ranking fuzzy numbers based on their corresponding centroidindex. In 1981, the same author published another article proposing a method for ordering fuzzy subsets based on the integral of the mean of the level sets [20]. His method was capable of comparing crisp numbers, discrete fuzzy subsets, and continuous fuzzy subsets of the unit interval. In 1993, Choobineh and Li [13] introduced a fuzzy number ordering method, in which the membership function of fuzzy numbers did not need to be normal and convex. In addition, their index had some additional properties that made it more suitable for decisionmaking purposes. The concepts of expected interval and expected value were introduced in 1992 by Heilpern [21], who then used them to order fuzzy numbers. In 1998, Cheng [11] proposed a centroidindex ranking method for calculating the centroidpoint of a fuzzy number. In 2000, Yao and Wu [17] used the decomposition principle and the crisp ranking system on R to construct a ranking system for fuzzy numbers. They used the decomposition principle to rewrite each fuzzy number as the union of all of its cuts (where ) and then used the centroidpoint of these αcuts to calculate the signed distance between the two fuzzy numbers. In 2001, Chen and Lu [18] developed an approximate approach for ranking fuzzy numbers based on the left and right dominance. To do so, they first defined the right and left limits of all cuts for all fuzzy numbers and then defined the left and right dominance of a fuzzy number over another as the average difference of the left and right spreads. They then used an optimality index to introduce the dominance of a fuzzy number over another as the convex combination of its left and right dominance. In 2003, S.J. Chen and S.M. Chen [16] introduced a procedure for ordering trapezoidal fuzzy numbers based on the center of gravity. The centroidindex ranking method presented in 2005 by Yong and Qi [12] employed TOPSIS to order the trapezoidal fuzzy numbers. In 2006, Asady and Zendehnam [10] presented a method for ranking fuzzy numbers by distance minimization. The fuzzy number ranking method proposed in 2008 by Chen and Wang [22] used αcuts for this purpose.
This paper aims to use statistical Beta distribution for defuzzifying and ranking fuzzy numbers, since it is the only distribution function that is bounded to and is zero outside this interval [26]. To do so, we can consider the surface inscribed within the projection of fuzzy number in the interval as a statistical population. In addition, parameters of this distribution could be set such that the resulting left spread, right spread, and mode fully match their corresponding values in the fuzzy number transformed to the interval . We initiate the defuzzification process of a fuzzy number by obtaining the mean value of its corresponding Beta distribution. We then use simple arithmetic operations to determine the crisp number corresponding to that fuzzy number. We also provide a very simple algorithm for ordering fuzzy numbers based on their corresponding crisp real values and domains. The first stage of the algorithm is centripetal and considers the fuzzy number with greater corresponding real value as the greater fuzzy number, but when two fuzzy numbers have equal corresponding real values, it considers the one with righter intuitionistic position (no matter how slight) as the greater number. Our motivation for writing this paper is to provide an easy and tangible defuzzification method and a wellordered algorithm for the ranking of fuzzy numbers. The method presented in this paper satisfies all reasonable properties defined by Wang and Kerre [23, 24] for ordering of triangular fuzzy numbers, but the mathematical complexity obstructed the process of proving the last property for trapezoidal fuzzy numbers.
The rest of this paper is organized as follows. Section 2 presents a brief introduction to fuzzy and statistical concepts and operations used in the paper. It also presents two theorems that constitute the basis of our method. Section 3 describes the proposed method of defuzzifying trapezoidal and triangular fuzzy numbers using the mean value of Beta distribution. This section also provides a fuzzy number ranking algorithm as well as ordinal properties to which this ranking method is applicable. In Section 4, several examples of fuzzy number ordering performed by the proposed method are presented. Section 5 uses 2 numerical examples to compare the proposed method of fuzzy number ranking with other methods developed for this purpose. The paper ends with Section 6, presenting conclusions.
2. Preliminaries
This section reviews some basic fuzzy and statistical concepts used in the rest of the paper. Two theorems that provide the basis of our method for obtaining crisp numbers corresponding to fuzzy numbers are introduced later in this section.
2.1. Fuzzy Preliminaries
Definition 1 (see [10]). A fuzzy number is a fuzzy set in the form of that satisfies the following conditions:(1) is upper semicontinuous,(2) is zero outside the interval ,(3)there exist real numbers , such that and(3.1) is increasing on ,(3.2) is decreasing on ,(3.3), .If then the fuzzy number is called the triangular fuzzy number and is defined as follows:Moreover, the fuzzy number is called a trapezoidal fuzzy number and is defined as follows:The set of all fuzzy numbers is denoted by . Furthermore, the set of all numbers that belong to the universal set such that is known as the supporter set of the fuzzy number .
2.2. Statistical Preliminaries
Let be the sample space of a random trial with a given probability value and let be the random variable defined as a realvalued function on . If is a discrete random variable, the function for any specific value of within range of is called probability distribution. When is a continuous random variable, the function is known as probability density function of . Probability distributions and probability densities come in different types, including uniform density and Beta distribution just to name two.
The random variable is said to have Beta distribution if and only if its probability density is as follows:where and are Beta distribution parameters. The mean of Beta distribution is obtained as follows:
If and , then the curve of Beta function will be unimodal. When , the curve is said to have negative skewness and if , then the skewness is positive. For the curve of Beta function is called symmetric. To better understand the described concept, see Figure 1, where . Given that the curve of Beta distribution is a unimodal one, should have a unique solution. Solving the equation gives the following relation between Beta distribution parameters: where is the point at which has the maximum value. Hence given both values of and , the parameter can be obtained from (5) and then the mean value of Beta distribution can be calculated by (4).
Uniform density is a special case of Beta distribution. The random variable is said to have uniform density if and only if its probability density is as follows:
The mean value of uniform density can be obtained by the following equation:
Using the abovementioned statistical preliminaries, a crisp number belonging to the interval and corresponding to the triangular fuzzy number can be obtained by (4) and (5) and a crisp number in the interval and corresponding to the trapezoidal fuzzy number can be obtained by (4), (5), and (7). The following theorems can then be used to calculate the real number corresponding to each fuzzy number in its domain.
Theorem 2. Let and , and let be an interval on . Then for every there exists a unique number and vice versa.
Proof. Suppose that and let ; then we have .
The uniqueness of is proven by contradiction. Assuming that and are two distinct numbers () corresponding to , the following relationships between and and between and are established:The above equations imply that which contradicts the initial assumption; that is, . Thus, initial assumption is false and uniqueness of is proven.
Theorem 3. For two distinct intervals and where , assume that and . If , then .
Proof. Let and be two distinct intervals. Then only one of the following is true:(1) and ,(2) and ,(3) and .According to theorem’s assumption, we haveIn the first case, we have and . Assuming that , it is supposed for contradiction that . Relation (9) yields which contradicts the initial statement. Therefore the contradictory assumption is false, proving the claim. Statements and can be easily proved through similar arguments.
3. Using Beta Distribution for Defuzzifying and Ranking Fuzzy Numbers
In this section, for every fuzzy number , the mean value of its corresponding Beta distribution in its domain is considered as the crisp real number corresponding to . Then the proposed method, which is based on crisp ranking system on , is used to rank and order the fuzzy numbers.
3.1. Defuzzification of Triangular Fuzzy Numbers
Consider the triangular fuzzy number . To obtain the crisp real number corresponding to the triangular fuzzy number , we first project on the interval , which will be in the form of . Then we define the parameter corresponding to the Beta distribution as follows:
With the above definition, firstly, it is clear that and if , then the Beta distribution curve will be unimodal. Secondly, if , which denotes a symmetric triangular fuzzy number, then and the Beta distribution curve will be symmetric. Here, the left skewness of the Beta distribution curve is the left side spread of triangular fuzzy number divided by its support set.
In the Beta distribution corresponding to the projection of fuzzy number , we have and by using (10) and substituting it into (5), we get
We use (4), (5), and (11) as shown below to calculate the mean value of Beta distribution corresponding to the fuzzy number:
The real number , which is obtained (as shown below) by transferring from the interval to the interval , is considered as the real number corresponding to the fuzzy number :
Figure 2 shows the manner of projecting the triangular fuzzy number on the interval , and Figure 3 shows the manner of defining the Beta function corresponding to the projection of fuzzy number in the interval (0,1).
Remark 4. The crisp real number corresponding to the triangular fuzzy number is obtained from the following relation:
3.2. Defuzzification of Trapezoidal Fuzzy Number
Consider the trapezoidal fuzzy number . We use a combination of Beta distribution and uniform distribution to defuzzify this trapezoidal fuzzy number. To start, the interval is partitioned as shown below:where , , and . We define the triangular fuzzy numbers corresponding to and as and and then obtain their corresponding real numbers denoted by and as follows:In order to obtain the real number corresponding to the interval , we use the uniform distribution and calculate its mean value via relation (7). The mean of the real numbers , , and is considered as the crisp real number corresponding to the trapezoidal fuzzy number.
Remark 5. The crisp real number corresponding to the trapezoidal fuzzy number is obtained by the following relation:
3.3. The Fuzzy Number Ranking Algorithm
Consider the fuzzy numbers and . We take the following steps to order these numbers.
Step 1. Calculate the crisp real numbers and corresponding to and .
Step 2. (A)If , then .(B)If and , then .(C)If , , and , then .(D)If , , and , then .Note that, for trapezoidal fuzzy number, we consider and use the symbols , , and to denote “greater than,” “less than,” and “equals” relations between fuzzy numbers.
We consider the following reasonable properties for the ordering approach; see [23, 24].For an arbitrary finite subset of and , .For an arbitrary finite subset of and , , and , we should have .For an arbitrary finite subset of and , , and , we should have .For an arbitrary finite subset of and , inf supp sup supp, we should have .For an arbitrary finite subset of and , inf supp sup supp, we should have .Let and be two arbitrary finite subsets of in which and are in . We obtain the ranking order by Beta distribution method on if and only if by Beta distribution method on .Let , , , and be elements of . If , then .Let , , , and be elements of and . If , then .Let , , , and be elements of and 0. by Beta distribution method on implies by one on .While we could only prove the properties to for the trapezoidal fuzzy numbers, all of these properties are satisfied for the triangular fuzzy numbers.
In the next section, we use the proposed method to order several fuzzy numbers.
4. Numerical Examples
In this section, we first use the mean value of Beta distribution to obtain the crisp real numbers corresponding to three sets of fuzzy numbers, and then use the proposed algorithm to order these numbers.
Set 1. Consider three fuzzy numbers , , and as follows: The Beta distribution method yields their corresponding real numbers as follows:The proposed algorithm returns (see Figure 4).
Set 2. Consider the fuzzy numbers and . According to the proposed method, the crisp real numbers corresponding to and are equal , but as Figure 5 shows, based on the hatched area, the proposed algorithm returns .
Set 3. Consider the following four fuzzy numbers , , , and and their corresponding crisp real numbers obtained from Beta distribution method:It is clear that is greater than other fuzzy numbers, because is greater than other obtained crisp real numbers. Also . Meanwhile is less than and , so is the smallest fuzzy number in the set. Since , to determine their ordering we need to compare their values. Hence we have .
The proposed method determines the ordering of the above fuzzy numbers as (see Figures 6 and 7). As Figure 6 shows, although , based on the hatched area, the proposed algorithm announces that < .
5. The Comparison of Beta Distribution Method with Other Existing Methods
In this section we compare our method of ranking fuzzy numbers with other methods developed for this purpose. We do so by using two sets of fuzzy numbers.
Set 1. Consider the set of fuzzy numbers containing , , and . Table 1 shows the ranking of these three numbers according to the methods of Chen and Wang [22], Yong and Qi [12], and S.J. Chen and S.M. Chen [16], in addition to the results obtained by the proposed method. As can be seen, the proposed method returns equal crisp real numbers for and , but since , we conclude that . The proposed algorithm, which is based on the mean value of Beta distribution, ranks these three fuzzy numbers as contradicting the result of other ranking methods used for comparison (see Figure 8).

Set 2. Consider the following three fuzzy numbers , , and :The ranking of these three fuzzy numbers obtained by the methods proposed in [10, 13, 15, 17, 20, 23, 25] and the result of Beta distribution method are shown in Table 2.
The method proposed in this paper calculates the real numbers corresponding to fuzzy numbers , , and as 0.516, 0.533, and 0.5 and therefore determines the order of these fuzzy numbers as , which is only consistent with the distance method proposed by Wang, thus contradicting the results obtained via other methods. For a better understanding, see Figures 9 and 10.

6. Conclusion
In this paper, we proposed a simple method for obtaining the crisp real number corresponding to a fuzzy number by using the mean value of Beta distribution and showed that this crisp real number could be obtained via simple mathematical operations. We also introduced a novel algorithm for ranking and ordering fuzzy numbers and reviewed the reasonable properties established for this algorithm and ultimately showed that the proposed method is a complete methodology for ranking fuzzy numbers. In the end, we used numerical examples to demonstrate the performance of our method and then compared it with other methods developed with similar objectives.
Competing Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
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Copyright © 2016 A. Rahmani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.