Advances in Fuzzy Systems

Volume 2016, Article ID 6945184, 8 pages

http://dx.doi.org/10.1155/2016/6945184

## A New Method for Defuzzification and Ranking of Fuzzy Numbers Based on the Statistical Beta Distribution

Department of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, Iran

Received 25 March 2016; Revised 22 June 2016; Accepted 18 October 2016

Academic Editor: Rustom M. Mamlook

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.

#### 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 real-world decision-making 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 human-centered 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, decision-making, 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 IF-THEN 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 real-world 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 “zero-one” logic and utilized the dynamic infinite space between these values. In the mid-1980s, Japanese industrialists used this worthwhile concept to develop a scheme of fully automated subway controls, which demonstrated its real-world 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 centroid-index. 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 decision-making 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 centroid-index ranking method for calculating the centroid-point 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 centroid-point of these *α*-cuts 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 centroid-index 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 *α*-cuts 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 well-ordered 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 real-valued 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).