Advances in Fuzzy Systems

Volume 2014 (2014), Article ID 892363, 9 pages

http://dx.doi.org/10.1155/2014/892363

Research Article

An Extended Analytical Approach to Evaluating Monotonic Functions of Fuzzy Numbers

Arthur Seibel and Josef Schlattmann

Workgroup on System Technologies and Engineering Design Methodology, Hamburg University of Technology, 21073 Hamburg, Germany

Received 18 September 2013; Accepted 26 December 2013; Published 11 February 2014

Academic Editor: Ning Xiong

Copyright © 2014 Arthur Seibel and Josef Schlattmann. 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.

This paper presents an extended analytical approach to evaluating continuous, monotonic functions of independent fuzzy numbers. The approach is based on a parametric α-cut representation of fuzzy numbers and allows for the inclusion of parameter uncertainties into mathematical models.

1. Introduction

There is an increasing effort in the scientific community to provide suitable methods for the inclusion of uncertainties into mathematical models. One way to do so is to introduce parametric uncertainty by representing the uncertain model parameters as fuzzy numbers [1] and evaluating the model equations by means of Zadeh’s extension principle [2]. The evaluation of this classical formulation of the extension principle, however, turns out to be a highly complex task [3]. Fortunately, Buckley and Qu [4] provide an alternative formulation that operates on -cuts and is applicable to continuous functions of independent fuzzy numbers. Powerful numerical techniques have been developed to implement this alternative formulation [5]. These techniques are particularly suitable for very complex simulation models [6]. In engineering design [7], however, the mathematical equations are usually less complex, and hence analytical methods might be more suitable for the inclusion of parameter uncertainties into the computations. For this purpose, a practical analytical approach to evaluating continuous, monotonic functions of independent fuzzy numbers was introduced by the authors [8], which is based on the alternative formulation of the extension principle. In this paper, we extend this approach in terms of computational efficiency depending on certain monotonicity conditions.

An outline of this paper is as follows. In Section 2, we give a definition of fuzzy numbers and present two important types. In Section 3, we introduce the notion of a linguistic variable. In Section 4, we briefly recall Zadeh’s extension principle and introduce the alternative formulation based on α-cuts. In Section 5, we describe our extended analytical approach and give four illustrative examples. In Section 6, a practical engineering application is presented. Finally, in Section 7, some conclusions are drawn.

2. Fuzzy Numbers

Fuzzy numbers are a special class of fuzzy sets [9], which can be defined as follows [1].

A normal, convex fuzzy set over the real line is called fuzzy number if there is exactly one with and the membership function is at least piecewise continuous. The value is called the modal or peak value of .

It is important to note that some authors consider normal, convex fuzzy sets with a core interval also as fuzzy numbers [10]. In [3, 6], these types of fuzzy numbers are denoted as fuzzy intervals. Furthermore, some authors define a fuzzy number having a compact support [11]. Although all concepts presented in this paper can be extended to these definitions of fuzzy numbers, we stick to the definition from [1].

Theoretically, an infinite number of possible types of fuzzy numbers can be defined. However, only few of them are important for engineering applications [6]. These typical fuzzy numbers shall be described in the following.

2.1. Triangular Fuzzy Numbers

Due to its very simple, linear membership function, the triangular fuzzy number (TFN) is the most frequently used fuzzy number in engineering. In order to define a TFN with the membership function

we use the parametric notation [6]

where denotes the modal value, denotes the left-hand, and denotes the right-hand spread of (cf. Figure 1). If , the TFN is called symmetric. Its -cuts result from the inverse functions of (1) with respect to :

Figure 1: Triangular fuzzy number.

2.2. Gaussian Fuzzy Numbers

Another widely used fuzzy number in engineering is the Gaussian fuzzy number (GFN), which is based on the normal distribution from probability theory. In order to define such a GFN with the membership function

we use the parametric notation [6]

where denotes the modal value, denotes the left-hand, and denotes the right-hand standard deviation of (cf. Figure 2). If , the GFN is called symmetric. Its -cuts result in

Figure 2: Gaussian fuzzy number.

3. Linguistic Variables

In decision analysis, linguistic variables are of particular importance [12].

A linguistic variable is a collection of subsets containing the following elements:

(i) set of syntactic rules (e.g., in terms of a grammar) for the linguistic quantification of ;(ii): set of terms , , resulting from ;(iii): set of semantic rules that assign every term to its (physical) meaning in terms of a fuzzy number ;(iv): (physically relevant) universal set with the (crisp) elements .

Figure 3 illustrates a possible description of the linguistic variable color. It is based on the continuous spectrum of the wave length of visible light:  nm. By subjective color perception, the colors are chosen from the set violet, blue, cyan, green, yellow, red} of possible colors. Each term is represented as a fuzzy number over the universal set .

Figure 3: Possible description of the linguistic variable color according to [6].

For an easier handling with linguistic variables, they can be transformed into the unit interval . These types of linguistic variables are referred to as normalized linguistic variables [12].

4. Extension Principle

Zadeh’s extension principle [2] allows for extending any real-valued function to a function of fuzzy numbers. More specifically, let be independent or noninteractive fuzzy numbers, and let be a function with . The fuzzy extension is then defined by

In case of interdependency between , the minimum operator should be replaced by a suitable triangular norm [13]. In this paper, however, we restrict ourselves to independent fuzzy numbers.

The evaluation of this classical formulation of the extension principle turns out to be a highly complex task [3]. Fortunately, Buckley and Qu [4] provide an alternative formulation that operates on -cuts.

Let denote the -cuts of the independent fuzzy numbers , and let be continuous. Then, the -cuts of can be computed from

where represent the -dimensional interval boxes that are spanned by the -cuts .

The extended analytical approach, which is presented in the next section, is based on this alternative formulation of the extension principle.

5. Extended Analytical Approach

Basically, our extended analytical approach can be classified into three parts depending on the monotonicity of : a reduced [8], a general [8], and an extended part.

5.1. Reduced Part

Let the continuous function be (strictly) monotonic increasing in , , and (strictly) monotonic decreasing in , , in the domain of interest, and let . Then, the minimum values of inside of every subdomain are always found at the left boundaries of and the right boundaries of and its maximum values at the right boundaries of and the left boundaries of , respectively. In such case, the -cuts of become

with , . If (9) is invertible with respect to , then the membership function of yields

This reduced part of our approach can be viewed as an analytical version of the short transformation method [14]. Basically, it is equivalent to Lemma 3 from [15] or Corollary 2 from [16].

Example 1. The function with

shall be evaluated for the two fuzzy numbers and . Since

the function is (strictly) monotonic increasing in and (strictly) monotonic decreasing in in the domain . Hence, the -cuts of are

With , , and , the membership function of yields

5.2. General Part

Unfortunately, the reduced part of our approach is only valid if the function does not change its monotonicity within the domain of interest. However, we know from [17, 18] that the global extrema of any monotonic function are always found at the corner points of . Hence, in order to obtain the analytical solution, we can always proceed as follows.

(1)Evaluate the function for all the permutations of the interval boundaries of , . For example, if , then compute

(2)Plot these solution candidates in the same diagram.(3)The analytical solution then corresponds to the maximum envelope formed by the possible solution candidates.

This general part of our approach can be viewed as an analytical version of the reduced transformation method [19]. Basically, it is equivalent to Lemma 2 from [15] or Corollary 1 from [16].

Example 2. Next, the function with

shall be evaluated for the two fuzzy numbers from Example 1. Since

the function changes its monotonicity within the domain . Hence, the general part of our approach should be applied. The solution candidates for are

We can see from their plots in Figure 4 that the left branch of the maximum envelope, illustrated by the gray area, is formed by for and by for , where the value corresponds to the intersection point between and . Its right branch, on the other hand, is formed by for and by for , where the value corresponds to the intersection point between and . Note that the value 0.02 is only approximate. Hence, the -cuts of are

Figure 4: Solution candidates from Example 2.

With , , , , and , the membership function of yields

where

5.3. Extended Part

A drawback of the general part of our approach is the fact that a total of function evaluations have to be carried out to compute the possible solution candidates. However, if some of the variables do not change their monotonicity within the domain of interest, that is, if , we can adapt our approach as follows.

(1)Evaluate the function for , , and , , including all the permutations of the interval boundaries of , , to compute the solution candidates for .(2)Evaluate the function for , , and , , including all the permutations of the interval boundaries of , , to compute the solution candidates for .(3)Plot these solution candidates in the same diagram.(4)The analytical solution then corresponds to the maximum envelope formed by the possible solution candidates.

This extended part of our approach requires a total of function evaluations. Note that, for , the general and the extended part both lead to function evaluations.

Example 3. Now, the function with

shall be evaluated for the two fuzzy numbers from Example 1. Since

the function is (strictly) monotonic increasing in but changes its monotonicity in within the domain . Hence, the extended part of our approach should be applied. Note that here, . The solution candidates for are

and for ,

We can see from their plots in Figure 5 that the left branch of the maximum envelope is formed by for and by for , where the value corresponds to their intersection point. Its right branch, on the other hand, is entirely formed by . Hence, the -cuts of are

Figure 5: Solution candidates from Example 3.

With , , , and , the membership function of yields

Example 4. Finally, the function with

shall be evaluated for the two fuzzy numbers from Example 1 and . Since

the function is (strictly) monotonic increasing in both and but changes its monotonicity in within the domain . Hence, the extended part of our approach should be applied. The solution candidates for are

and for ,

We can see from their plots in Figure 6 that the left branch of the maximum envelope is formed by for and by for , where the value corresponds to their intersection point. Its right branch, on the other hand, is entirely formed by . Hence, the -cuts of are

Figure 6: Solution candidates from Example 4.

With , , , and , the membership function of yields

where

6. Engineering Application

In order to illustrate the extended analytical approach in a more practical context, we consider a simplified version of the case study from [20], where the material for an automotive bumper beam has to be selected. Here, two alternative materials (polymer composite and aluminum alloy) have to be evaluated against the criteria low weight and low cost using the normalized linguistic variable value scale from Figure 7. The corresponding linguistic weights and ratings are summarized in Table 1.

Table 1: Linguistic weights of the criteria and ratings of the alternative materials.

Criterion

1
2

Low weight
Low cost

Weight

H
MH

Alternative
1
Polymer composite
H
L

2
Aluminum alloy
MH
L

Figure 7: Normalized linguistic variable value scale. VL: very low, L: low, ML: medium low, M: medium, MH: medium high, H: high, and VH: very high.

For computing the fuzzy overall rating of each alternative , we use the fuzzy weighted average

where denotes the fuzzy rating of the alternative according the criterion and denotes the fuzzy weight of the criterion (see Table 1). Since

is (strictly) monotonic increasing in but may change its monotonicity in within the domain . Hence, the extended part of our approach should be applied. The solution candidates for are

and for ,

We can see from their plots in Figure 8 that the left branch of the maximum envelope is formed by and the right branch by . Hence, the -cuts of are

Figure 8: Solution candidates for .

With , , and , the membership function of yields

Furthermore, the solution candidates for are

and for ,

We can see from their plots in Figure 9 that the left branch of the maximum envelope is formed by and the right branch by . Hence, the -cuts of are

Figure 9: Solution candidates for .

With , , and , the membership function of yields

The membership functions of and are illustrated in Figure 10. There, we can see that polymer composite seems to be more appropriate as bumper beam material than aluminum alloy.

Figure 10: Membership functions of and .

7. Conclusions

The proposed extended analytical approach is a very practical tool for the inclusion of parameter uncertainties into mathematical models. It is valid for continuous, monotonic functions of independent fuzzy numbers but can also be applied to fuzzy intervals as defined in [3, 6].

An analytical solution has the advantage that the degrees of membership of the fuzzy output can be computed for any value within the support, whereas a numerical solution only provides a finite number of values. Furthermore, our approach also allows a symbolic processing of uncertainties.

In further research activities, this approach shall be generalized to nonmonotonic functions of independent fuzzy numbers, where the influence of interdependency shall be investigated as well.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This publication was supported by the German Research Foundation (DFG) and Hamburg University of Technology (TUHH) in the funding programme “Open Access Publishing.”

References

D. Dubois and H. Prade, Fuzzy Sets and Systems: Theory and Applications, Academic Press, New York, NY, USA, 1980.

L. A. Zadeh, “The concept of a linguistic variable and its application to approximate reasoning-I,” Information Sciences, vol. 8, no. 3, pp. 199–249, 1975.

A. Klimke, Uncertainty modeling using fuzzy arithmetic and sparse grids [Ph.D. thesis], University of Stuttgart, 2006.

J. J. Buckley and Y. Qu, “On using α-cuts to evaluate fuzzy equations,” Fuzzy Sets and Systems, vol. 38, no. 3, pp. 309–312, 1990.

D. Moens and M. Hanss, “Non-probabilistic finite element analysis for parametric uncertainty treatment in applied mechanics: recent advances,” Finite Elements in Analysis and Design, vol. 47, no. 1, pp. 4–16, 2011.

M. Hanss, Applied Fuzzy Arithmetic: An Introduction with Engineering Applications, Springer, Berlin, Germany, 2005.

G. Pahl, W. Beitz, J. Feldhusen, and K. H. Grote, Engineering Design: A Systematic Approach, Springer, London, UK, 2007.

A. Seibel and J. Schlattmann, “An analytical approach to evaluating monotonic functions of fuzzy numbers,” in Proceedings of the 8th Conference of the European Society for Fuzzy Logic and Technology, pp. 289–293, Milano, Italy, 2013.

L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965.

A. Kaufmann and M. M. Gupta, Introduction to Fuzzy Arithmetic, Van Nostrand Reinhold, New York, NY, USA, 1991.

G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice Hall PTR, Upper Saddle River, NJ, USA, 1995.

B. Möller and M. Beer, Fuzzy Randomness: Uncertainty in Civil Engineering and Computational Mechanics, Springer, Berlin, Germany, 2004.

K. Scheerlinck, Metaheuristic versus tailor-made approaches to optimization problems in the biosciences [Ph.D. thesis], Ghent University, 2011.

S. Donders, D. Vandepitte, J. van de Peer, and W. Desmet, “Assessment of uncertainty on structural dynamic responses with the short transformation method,” Journal of Sound and Vibration, vol. 288, no. 3, pp. 523–549, 2005.

H. Q. Yang, H. Yao, and J. D. Jones, “Calculating functions of fuzzy numbers,” Fuzzy Sets and Systems, vol. 55, no. 3, pp. 273–283, 1993.

J. Fortin, D. Dubois, and H. Fargier, “Gradual numbers and their application to fuzzy interval analysis,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 2, pp. 388–402, 2008.

W. M. Dong and F. S. Wong, “Fuzzy weighted averages and implementation of the extension principle,” Fuzzy Sets and Systems, vol. 21, no. 2, pp. 183–199, 1987.

W. Dong and H. C. Shah, “Vertex method for computing functions of fuzzy variables,” Fuzzy Sets and Systems, vol. 24, no. 1, pp. 65–78, 1987.

M. Hanss, “The transformation method for the simulation and analysis of systems with uncertain parameters,” Fuzzy Sets and Systems, vol. 130, no. 3, pp. 277–289, 2002.

L. V. Vanegas and A. W. Labib, “Application of new fuzzy-weighted average (NFWA) method to engineering design evaluation,” International Journal of Production Research, vol. 39, no. 6, pp. 1147–1162, 2001.