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010   $a9783319247533
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024 7 $a10.1007/978-3-319-24753-3
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035   $a(OCoLC)1066179699
035   $a(PPN)190530014
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100   $a20151014d2016      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt
182   $cc
183   $acr
200 12$aA Practical Guide to Averaging Functions /$fby Gleb Beliakov, Humberto Bustince Sola, Tomasa Calvo
205   $a1st ed. 2016.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016.
215   $a1 online resource (XIX, 352 pages $cillustrations (color.)
225 1 $aStudies in Fuzziness and Soft Computing,$x1434-9922 ;$v329
300   $aBibliographic Level Mode of Issuance: Monograph
311 08$a9783319247519 
311 08$a3319247514 
330   $aThis book offers an easy-to-use and practice-oriented reference guide to mathematical averages. It presents different ways of aggregating input values given on a numerical scale, and of choosing and/or constructing aggregating functions for specific applications. Building on a previous monograph by Beliakov et al. published by Springer in 2007, it outlines new aggregation methods developed in the interim, with a special focus on the topic of averaging aggregation functions. It examines recent advances in the field, such as aggregation on lattices, penalty-based aggregation and weakly monotone averaging, and extends many of the already existing methods, such as: ordered weighted averaging (OWA), fuzzy integrals and mixture functions. A substantial mathematical background is not called for, as all the relevant mathematical notions are explained here and reported on together with a wealth of graphical illustrations of distinct families of aggregation functions. The authors mainly focus on practical applications and give central importance to the conciseness of exposition, as well as the relevance and applicability of the reported methods, offering a valuable resource for computer scientists, IT specialists, mathematicians, system architects, knowledge engineers and programmers, as well as for anyone facing the issue of how to combine various inputs into a single output value.
410  0$aStudies in Fuzziness and Soft Computing,$x1434-9922 ;$v329
606   $aComputational intelligence
606   $aAutomatic control
606   $aRobotics
606   $aMechatronics
606   $aArtificial intelligence
606   $aOperations research
606   $aManagement science
606   $aComputational Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/T11014
606   $aControl, Robotics, Mechatronics$3https://scigraph.springernature.com/ontologies/product-market-codes/T19000
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aOperations Research, Management Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M26024
615  0$aComputational intelligence.
615  0$aAutomatic control.
615  0$aRobotics.
615  0$aMechatronics.
615  0$aArtificial intelligence.
615  0$aOperations research.
615  0$aManagement science.
615 14$aComputational Intelligence.
615 24$aControl, Robotics, Mechatronics.
615 24$aArtificial Intelligence.
615 24$aOperations Research, Management Science.
676   $a515.724
700   $aBeliakov$b Gleb$4aut$4http://id.loc.gov/vocabulary/relators/aut$0760797
702   $aBustince$b Humberto$4aut$4http://id.loc.gov/vocabulary/relators/aut
702   $aCalvo$b Tomasa$4aut$4http://id.loc.gov/vocabulary/relators/aut
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910254188903321
996   $aA Practical Guide to Averaging Functions$92535811
997   $aUNINA