Download e-book for kindle: Fuzzy Reasoning in Decision Making and Optimization by Christer Carlsson, Robert Fuller

By Christer Carlsson, Robert Fuller

ISBN-10: 3790818054

ISBN-13: 9783790818055

ISBN-10: 3790824976

ISBN-13: 9783790824971

This ebook begins with the elemental recommendations of fuzzy arithmetics and progresses in the course of the research of sup-t-norm-extended mathematics operations, possibilistic linear structures and fuzzy reasoning methods to fuzzy optimization. 4 functions of (interdependent) fuzzy optimization and fuzzy reasoning to strategic making plans, venture administration with genuine ideas, strategic administration and provide chain administration are offered and thoroughly mentioned. The ebook ends with an in depth description of a few clever software program brokers, the place fuzzy reasoning schemes are used to reinforce their performance. it may be invaluable for researchers and scholars operating in tender computing, utilized arithmetic, operations examine, administration technological know-how, details structures, clever brokers and synthetic intelligence.

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14. Pos[A is true, denoted by Pos[A ~ Nes[A If A = (a, a) and B = ~ B] .. < 1, because a > b. B], is defined as ~ B] = 1 - Pos[A ~ B]. 10 Measures of possibility and necessity Fig. 15. Nes[A ~ B] then < 1, I (a < b, An B # 0). 23) ifa~b F be a fuzzy number. Given a subset D c lR, the grade of possibility Fig. 16. Nes[A ~ B] = 1, (a < b and An B = 0). 24) The quantity 1- Pos(~ I D), where D is the complement of D, is denoted by Nes(~ I D) and is interpreted as the grade of necessity of the statement "D contains the value of C.

We need to determine the value of a2(Z) from the following relationships: A2(Z) = (0,1 + (2)(Z) = sup x+y=z, + al(X)a2(Y) . _ _ _ _ , If IA2 (1 - ,)(al(x) + a2(Y) - al(x)a2(Y)) zl < 2a, and A2(Z) = 0 otherwise. According to the decomposition rule of fuzzy numbers into two separate parts, A2(Z), A2 -2a < z :::; A 2, is equal to the optimal value of the following mathematical programming problem: ¢(x) ----. max subject to {al - a where ¢(x) < x :::; al, a2 - a < z - x :::; a2}, = [1 - (al - x)/a][l - (a2 - z + x)/a] ,+ (1 - ,){2 - (al + a2 - z)/a - [1 - (al - x)/a][l - (a2 - z + x)/a]r Using Lagrange's multipliers method for the solution of the above problem we get that its optimal value is 26 1.

Z) = f[-IJ (n. f(M(z))) x < 1 and lim f[-I J (x) x-+oo = 0, we get lim e~(M)] (z) [n-+CX) . hm n-+oo = n-+oo lim (e~(M))(z) = f [-I J ( n . 16) which is the peak of M. 16) remains valid for the (non-Archimedean) weak t-norm. a where f : X x Y ----) Z, T is a t-norm, A and B are fuzzy subsets of X and Y, respectively, f(A, B) is defined via sup-T-norm convolution, [A]a and [B]a are the a-level sets of A and B, respectively, and [f(A, B)]a is the a-level set of f(A,B). 34 1. Fuzzy Sets and Fuzzy Logic Furthermore, we shall define a class of fuzzy subsets in which this equality holds for all upper semicontinuous T and continuous f.

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Fuzzy Reasoning in Decision Making and Optimization by Christer Carlsson, Robert Fuller


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