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 bargains 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 features for particular functions. development on a prior monograph via Beliakov et al. released through Springer in 2007, it outlines new aggregation equipment constructed in the intervening time, with a unique concentrate on the subject of averaging aggregation features. It examines fresh advances within the box, corresponding to aggregation on lattices, penalty-based aggregation and weakly monotone averaging, and extends a number of the already latest equipment, resembling: ordered weighted averaging (OWA), fuzzy integrals and blend features. a considerable mathematical historical past isn't referred to as for, as the entire correct mathematical notions are defined the following and stated on including a wealth of graphical illustrations of distinctive households of aggregation services. The authors commonly concentrate on sensible purposes and provides important significance to the conciseness of exposition, in addition to the relevance and applicability of the suggested equipment, providing a beneficial source for laptop scientists, IT experts, mathematicians, process architects, wisdom engineers and programmers, in addition to for someone dealing with the problem of ways to mix quite a few inputs right into a unmarried output value.

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Extra info for A Practical Guide to Averaging Functions

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X1n , . . , xmn ) we can either evaluate the scores given by the i-th juryman, yi = f n (xi1 , . . , xin ), and then aggregate them as z = f m (y1 , . . , compute y˜ j = f m (x1 j , . . , xm j ), and then aggregate these scores as z˜ = f n ( y˜1 , . . , y˜n ). The third alternative is to aggregate all the scores by an aggregation function f mn (x). 1. We can either aggregate scores in each row, and then aggregate the totals in the last column of this table, or we can aggregate scores in each column, and then aggregate the totals in the last row, or aggregate all scores at once.

A few recently developed methods will also be presented, and we will provide the references to the articles which discuss these methods in detail. 1 Univariate Approximation and Smoothing Consider a set of data (xk , yk ), k = 1, . . , K , xk , yk ∈ R. The aim of interpolation is to define a function f , which can be used to calculate the values at x distinct from xk . The interpolation conditions are specified as f (xk ) = yk for all k = 1, . . , K . We assume that the abscissae are ordered xk < xk+1 , k = 1, .

Xi−1 , a, xi+1 , . . , xn ) = a, for every x such that xi = a with a in any position. 31 An absorbing element, if it exists, is unique. It can be any number from I. 32 Any conjunctive aggregation function has absorbing element a = 0. Any disjunctive aggregation function has absorbing element a = 1. 9. Some averaging functions also have an absorbing element, for example the geometric mean 1/n n f (x) = xi i=1 has the absorbing element a = 0. Assume f has two neutral elements e and u. Then u = f (e, u) = e, therefore e = u.

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