A COMPUTATIONAL METHOD FOR FUZZY ARITHMETIC OPERATIONS

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1 8 DAFFODIL INTERNATIONAL UNIVERSITY JOURNAL OF SCIENCE AND TECHNOLOGY, VOLUME 4, ISSUE, JANUARY 009 A COMPUTATIONAL METHOD FOR FUZZY ARITHMETIC OPERATIONS Thowhida Akther and Sanwar Uddin Ahmad Faculty of Science and Information Technology Daffodil International University, Dhaka, Bangladesh Department of Mathematics University of Dhaka, Dhaka, Bangladesh and Abstract: In this paper, a computer implementation to evaluate the arithmetic operations on two fuzzy numbers with linear membership functions has been developed The fuzzy arithmetic approached by the interval arithmetic is used here The algorithm of the developed method with a numerical eample is also provided Using this method four basic arithmetic operations between any two TFNs can be evaluated without compleity Keywords: Fuzzy arithmetic, Fuzzy number, Membership Function, Interval arithmetic, - cut Introduction The fuzzy numbers were introduced by [] and frequently arise in decision making, control theory, fuzzy systems and approimate reasoning problems The fuzzy arithmetic was developed by several later authors, eg [ 4] In recent years some research on the application of the fuzzy set theory [5] in education, based on the concepts of fuzzy numbers and fuzzy arithmetic has begun eg, [6, 7] Fuzzy numbers are also used in statistics, computer programming, engineering (especially communications), and eperimental science In calculations generally for computational efficiency many applications limit the membership functions of the fuzzy numbers to triangular or trapezoidal fuzzy numbers Using the inverse function [4] the implementation of arithmetic operations on fuzzy numbers is computationally comple and the implementation of the etension principle [5] is equivalent to solving a nonlinear programming problem A direct, fast and accurate way of computing the arithmetic operations on fuzzy numbers has engaged researchers and students of many fields and became the motivation to produce the proposed computational method The users can evaluate the eact form of the membership functions of the resultant fuzzy numbers in a simple and accurate implementation on computer using MATHEMATICA In this paper the triangular fuzzy numbers (TFN) are considered The fuzzy arithmetic approached by interval arithmetic for fuzzy numbers has been used in this paper In recent times [8] proposed a method using spreadsheet to evaluate the arithmetic operations on fuzzy numbers and [9] proposed a method using MATHEMATICA to evaluate the multiplication on fuzzy numbers In this paper the method proposed in [9] is etended for all the four basic arithmetic operations on fuzzy numbers and evaluate the eact forms of the membership functions of the fuzzy results Fuzzy Intervals and Numbers The universe of discourse on which fuzzy numbers are defined is the set of real numbers and its subsets (eg, integers or natural numbers), its membership function should assign the degree of to the central value and degrees to other numbers that reflects their proimity to the central value according to some rule therefore the membership functions ought to be normal and conve The membership should thus decrease from to 0 on both sides of the central value Fuzzy sets of this kind are known as fuzzy numbers Membership functions that conform to this intuitive conception must be epressed in the general form [0],

2 DAFFODIL INTERNATIONAL UNIVERSITY JOURNAL OF SCIENCE AND TECHNOLOGY, VOLUME 4, ISSUE, JANUARY f ( ), for [ a, b], for [ b, c] A( ) g( ), for [ c, 0, where a b c d, f is increasing and rightcontinuous function on [a, b], and g is decreasing and left-continuous function on [c, If b = c then A is a fuzzy number it is known as fuzzy interval When a d f ( ) = and g( ) = the fuzzy b a d c number A is a trapezoidal fuzzy number When f and g are as before and b = c then A is a triangular fuzzy number (TFN) 0 c Fig A trapezoidal fuzzy number 0 a b = c Fig A triangular fuzzy number The TFN A is denoted by A =< a, b, d > The -cuts of the TFN A are [ a + ( b a), d ( d b)], [0,] () 3 Standard Fuzzy Arithmetic Operations The essential properties for defining meaningful arithmetic operations on fuzzy numbers are, (a) Fuzzy numbers are normal fuzzy sets, ie X st A ( ) = (b) The -cuts of every fuzzy number are closed intervals of real numbers (0,] (c) The support of every fuzzy number, S(A) = Supp(A) = { IR > 0} is bounded (d) Fuzzy numbers are conve fuzzy sets ie for all, X, A( λ + ( λ) ) min( A( ), A( )) For computing the four basic arithmetic operations on fuzzy numbers we represent the d d numbers by their -cuts and employ interval arithmetic to the -cuts Consider two arbitrary fuzzy numbers A and B, and let denote any of the four arithmetic operations Then, for each (0, ], the -cut of A B is defined in terms of the -cuts of A and B by the formula ( A B) = A B () which is not applicable when is division and 0 B for any (0,] Once the -cuts ( A B) are determined, the resulting fuzzy number A B is readily epressed by the decomposition theorem of fuzzy sets as, A B = ( A B) (3) [0,] Since every fuzzy set is uniquely represented by its -cuts and these are closed intervals of real numbers, arithmetic operations on fuzzy numbers can defined in terms of arithmetic operation on closed intervals of real numbers which are described in the net section 4 Interval Arithmetic The arithmetic operations for intervals [, ], based on the respective real arithmetic operations at the etremes of the intervals are defined as follows, Let T = [a, b] and S = [c, be two closed intervals of real numbers, then T S = {: there is some y in T, and some z in S, such that = y z}, where is the arithmetic operations ecept division (/), which is not defined if 0 S The arithmetic operations on intervals are defined in the ends of their intervals, as is shown below [], Addition (+): T + S = [ a, b] + [ c, = [ a + c, b + Subtraction ( ): T - S = [ a, b] [ c, = [ a d, b c] Multiplication (): T S = [a, b] [c, = [min{ac, ad, bc, bd}, ma{ac, ad, bc, bd}] More precisely the etremes of the multiplied interval is provided in the following table Division (/): a b [ a, b]/[ c, = [min(,,, ), c d c d a b ma(,,, )], provided 0 [ c, c d c d The etremes of the resultant interval are given in the following table

3 0 AKTHER ET AL: A COMPUTATIONAL METHOD FOR FUZZY ARITHMETIC OPERATIONS Table Table for computing the etremes of the product of two closed intervals T = [ a, b] S = [ c, T S 0 a b 0 c d [ac, b c 0 d [bc, b c d 0 [bc, a 0 c d [ad, c a 0 b c 0 d [min(ad, bc), ma(ac, bd)] c d 0 [bc, ac] 0 c d [ad, bc] a b 0 c 0 d [ad, ac] c d 0 [bd, ac] Table Table for computing the etremes of the division of two closed intervals T = [a, b] S = [ c, T / S 0 a b a 0 b a b 0 d c b a d c c c b a d d c d b b c d 5 Algorithm for the proposed method In this section the algorithm of the developed MATHEMATICA code for the fuzzy arithmetic of two TFNs which evaluate the membership function of the resultant fuzzy number is provided Given A = <a, f A, b, g A, c >, B = <a, f B, b, g B, c > Input: The real numbers a, b, c, a, b, c The functions f A (), [ a, b ], g A (), [ b, c ], f B (), [ a, b ], g B (), [ b, c ] Output: Evaluate the analytical form of the membership function of (A+B)(), (A B)(), (AB)(), (A/B)() Step Find A = [ A, A ], B = B, B ] in terms of [ If = Addition, Then ( A + B) = [ A + B, A + B ] by Section 4 and go to Step If = Subtraction, Then ( A B) = [ A B, A B ] by Section 4 and go to Step Step Using backward transformation technique, compute the membership functions of (A B)() in terms of for ( A B) (0), ( A ) ()] and [ B [ ( A B) (0), ( A B) ()] respectively If = Division and 0 [ B, B ] Then go to Step3 Else Output Division is not defined End If If = Multiplication [9] Then go to Step3 Step 3 Define functions to compute the etremes ( A B), ( A B) of ( A B), for [ m, n] in terms of, m, n Step 4 Define functions using backward transformation technique, which compute the membership functions of (A B)() in terms of for ( A B) ( m), ( A B) ( )] and [ n [ ( A B) ( m), ( A B) ( n Step 5 Let K = Min{A(0), B(0)} and K = Ma{A(0), B(0)} )] respectively If = Division go to Step 5 If = Multiplication [9] go to Step 6 Step 6 Case If K = 0 or K = Compute (A/B)() for 0 < < Case If 0 < K < Compute (A/B)() for 0 < < K and K < < respectively Step 7 Case If K = 0 and 0 < K < Compute (AB)() for 0 < < K, and K < < respectively Case If (K = 0 and K = ) or K = 0 Compute (AB)() for 0 < < Case3 If K > 0 and 0 < K < Compute (AB)() for 0 < < K, K < < K and K < < respectively Case4 If K =

4 DAFFODIL INTERNATIONAL UNIVERSITY JOURNAL OF SCIENCE AND TECHNOLOGY, VOLUME 4, ISSUE, JANUARY 009 Compute (AB)() for 0 < < Case5 If 0 < K < and K = Compute (AB)() by Case3 6 Numerical Eample Consider two TFN s with membership functions +, 3 A( ), 3, 0,, 3 5 and B( ), 3 5 0, Fig 3 Membership functions of and B() The membership function for the addition of, 9 < 3 addab ( ), B() Fig 4 Membership function of (A + B)() The membership function for the subtraction of, < 3 subab ( ) 0 +, Fig 5 Membership function of (A - B)() The membership function for the product of ( ), 0 8 AB ( ) ( ), 0 < 35 4 ( ), 0 < Fig 6 Membership function of (AB)() The membership function for the division of 3( + ), divab ( ), < , 0 < 7 + 5

5 AKTHER ET AL: A COMPUTATIONAL METHOD FOR FUZZY ARITHMETIC OPERATIONS Fig 7 Membership function of (A/B)() 7 Conclusions This paper presents a direct, fast and accurate way of computing the arithmetic operations on fuzzy numbers as well as the analytic form of the resultant membership functions The implementation proposed by [8] is limited in finding the membership grade of a particular number, provides no information about the resultant fuzzy number (eg the shape, the support etc), while in [9] the authors implemented the method only for the multiplication of two TFN s with linear membership function This paper is the etension of [9], where the implementation for addition, subtraction and division of two TFN s is provided Users can evaluate the four basic arithmetic operations between any two TFNs without compleity and even plot the membership function of the resultant fuzzy number to take any further decisions (eg, shape, support, -cuts etc) References [] L A Zadeh, The Concept of linguistic variable and its applications to approimate reasoning, Inform Sci Vol 8, 975, pp 99-5 [] M MizuMoto, K Tanaka, The four operations on fuzzy numbers, Syst Compute Controls, Vol 7, 976, pp 73-8 [3] D Dubois, H Prade, Operation on fuzzy numbers, Int J Syst Sci, Vol 9, 978, pp [4] R R Yager, On the lack of inverses in fuzzy arithmetic, Fuzzy Sets Syst, Vol 4, 980, pp 73-8 [5] LA Zadeh, Fuzzy Sets, Information and Control, Vol 8, 965, pp [6] Chih-Huang Wang, Shy-Ming Chen, Appraising the performance of high school teachers based on fuzzy number arithmetic operations, Soft Comput, Vol, 008, pp [7] Law CK, Using fuzzy numbers in education grading system, Fuzzy Sets Syst, Vol 83, No 3, 996, pp 3-33 [8] PS Kechagias, Basil K Papadopoulos, Computational method to evaluate fuzzy arithmetic operations, Applied Mathematics and Computations, Vol 85, 007, pp [9] Sanwar Uddin Ahmad and Thowhida Akther, Multiplication of Fuzzy Numbers Using Mathematica, Daffodil Int Univ J of Sc & Tech, Vol 3, Issue, pp 3 35, 008 [0] GJKlir, BYuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall PTR, Upper Saddlie River, 995 [] Moore, R E, Interval Analysis, Prentice Hall, New Jersey, 966 [] Oliveira, P W de, Fundamentos de Matemática Intervalar, a ed Instituto de Informática da UFRGS: SAGRA-Luzzato, 997 Thowhida Akther has received her MS in Applied Mathematics and BSc (hons) in Mathematics from University of Dhaka Now she is working as a Lecturer under the Department of CSE, CIS & CS in Daffodil International University, Dhaka Her interested field of research includes Numerical Methods, Operations Research and Fuzzy Mathematics Sanwar Uddin Ahmad has completed MS in Applied Mathematics and BSc (hons) in Mathematics from University of Dhaka Now he is working as a Lecturer in the Department of Mathematics, University of Dhaka His areas of research are Operations Research, Fuzzy Mathematics and Group Theory

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