<?xml version="1.0" encoding="UTF-8"?>
<XML><RECORDS>
<RECORD>
	<REFERENCE_TYPE>0</REFERENCE_TYPE>
	<AUTHORS>
		<AUTHOR>Irina Perfilieva</AUTHOR>
		<AUTHOR>Didier Dubois</AUTHOR>
		<AUTHOR>Henri Prade</AUTHOR>
		<AUTHOR>Francesc Esteva</AUTHOR>
		<AUTHOR>Lluís Godo</AUTHOR>
		<AUTHOR>Petra Hodáková</AUTHOR>
	</AUTHORS>
	<YEAR>2012</YEAR>
	<TITLE>Interpolation of fuzzy data: Analytical approach and overview</TITLE>
	<SECONDARY_TITLE>Fuzzy Sets and Systems</SECONDARY_TITLE>
	<PUBLISHER>Elsevier</PUBLISHER>
	<VOLUME>192</VOLUME>
	<PAGES>134-158</PAGES>
	<DATE>04/2012</DATE>
	<KEYWORDS>
		<KEYWORD>Fuzzy</KEYWORD>
		<KEYWORD>function,</KEYWORD>
		<KEYWORD>Fuzzy</KEYWORD>
		<KEYWORD>space,</KEYWORD>
		<KEYWORD>Similarity,</KEYWORD>
		<KEYWORD>Interpolation</KEYWORD>
		<KEYWORD>of</KEYWORD>
		<KEYWORD>fuzzy</KEYWORD>
		<KEYWORD>data,</KEYWORD>
		<KEYWORD>Interpolating</KEYWORD>
		<KEYWORD>fuzzy</KEYWORD>
		<KEYWORD>function</KEYWORD>
	</KEYWORDS>
	<ABSTRACT>We propose a general framework for the interpolation problem. Our framework stems from the classical elaboration of the problem. We introduce the notion of an interpolating fuzzy function and show how this function can be characterized. We examine and analyze previously published fuzzy interpolation algorithms to choose those algorithms that can be represented analytically. We also propose an analytic solution of the interpolation problem that unifies various algorithmic approaches.</ABSTRACT>
</RECORD>
</RECORDS></XML>