Download A Practical Guide to Averaging Functions by Gleb Beliakov, Humberto Bustince Sola, Tomasa Calvo PDF

By Gleb Beliakov, Humberto Bustince Sola, Tomasa Calvo

This booklet deals an easy-to-use and practice-oriented reference consultant to mathematical averages. It offers other ways of aggregating enter values given on a numerical scale, and of selecting and/or developing aggregating services for particular purposes. construction on a prior monograph by means of Beliakov et al. released through Springer in 2007, it outlines new aggregation equipment constructed in the intervening time, with a unique specialize in the subject of averaging aggregation services. It examines contemporary advances within the box, akin to aggregation on lattices, penalty-based aggregation and weakly monotone averaging, and extends a few of the already present tools, similar to: ordered weighted averaging (OWA), fuzzy integrals and blend services. a considerable mathematical heritage isn't known as for, as the entire correct mathematical notions are defined right here and said on including a wealth of graphical illustrations of exact households of aggregation features. The authors ordinarily specialize in useful functions and provides valuable significance to the conciseness of exposition, in addition to the relevance and applicability of the mentioned tools, supplying a worthwhile source for machine scientists, IT experts, mathematicians, procedure architects, wisdom engineers and programmers, in addition to for a person dealing with the problem of ways to mix quite a few inputs right into a unmarried output value.

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Extra resources for A Practical Guide to Averaging Functions

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X(n) ) is a non-decreasing permutation of the input x, and x(n+1) = ∞ by convention. 20 A set function is a function whose domain consists of all possible subsets of N . For example, for n = 3, a set function is specified by 23 = 8 values at v(∅), v({1}), v({2}), v({3}), v({1, 2}), v({1, 3}), v({2, 3}), v({1, 2, 3}). 4 Main Families and Prototypical Examples 27 By rearranging the terms of the sum, Eq. 9) can also be written as n x(i) − x(i−1) v(Hi ). 10) i=1 where x(0) = 0 by convention, and Hi = {(i), .

B, xi , b . . , b) ≤ xi . This holds for every i, therefore x j ∈[a,b], j =i g(x) ≤ min(x). 86 we complete the proof. 90 will be mainly used when choosing f as an averaging aggregation function. 90: the value f (x) must be distinct from b for x = (b, b, . . , b). Furthermore, if max f (b, . . , b, a, b, . . ,n i-th position, then necessarily ψ(t) = a for t ≤ c. If ψ is a bijection, then f must have c = a as absorbing element. 90 is that one can construct conjunctive aggregation functions from many types of averaging functions (discussed in Chap.

Xn ) = f 2 ( f 2 (. . f 2 (x1 , x2 ), x3 ), . . , xn ). Thus bivariate associative aggregation functions univocally define extended aggregation functions. 40 The product, minimum and maximum are associative aggregation functions. The arithmetic mean is not associative. Associativity simplifies calculation of aggregation functions, and it effectively allows one to easily aggregate any number of inputs. It is not the only way of doing this (for instance the arithmetic or geometric means are also easily computed for any number of inputs).

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