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Monotonicity Preserving Rational Cubic Graph-Directed Fractal Interpolation Functions
Published in
2021
Volume: 1170
   
Pages: 253 - 267
Abstract
The idea of graph-directed fractal interpolation function (FIF) is introduced recently to represent several dependent data sets from graph-directed iterated function system (GDIFS). When dependent data sets are generated from C1-smooth functions with irregular derivatives, it is not ideal to use affine FIF or classical splines in such scenario. Thus, we initiate the use of smooth graph directed FIFs for two or more sets of interpolation data that are not independent, and generated from original smooth functions having fractal characteristics in their derivatives. For this task, we have proposed a new class C1-rational cubic graph-directed FIFs (RCGDFIFs) using cubic rational function involving two shape parameters in each sub-interval. For applications of the proposed RCGDFIFs in modeling of monotonic data sets, we have deduced sufficient condition based on the restriction of the corresponding rational GDIFS parameters. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About the journal
JournalAdvances in Intelligent Systems and Computing
Open AccessNo