Fractal Based Rational Cubic Trigonometric Zipper Interpolation with Positivity Constraints
This work addresses the need for positivity-preserving interpolation in data modeling, though it appears incremental as it builds on existing fractal and trigonometric methods.
The authors tackled the problem of preserving positivity in data interpolation by proposing a novel fractal-based interpolation scheme called Rational Cubic Trigonometric Zipper Fractal Interpolation Functions (RCTZFIFs), which effectively maintains positivity through constraints on scaling factors and shape parameters, as demonstrated in numerical experiments.
We propose a novel fractal based interpolation scheme termed Rational Cubic Trigonometric Zipper Fractal Interpolation Functions (RCTZFIFs) designed to model and preserve the inherent geometric property, positivity, in given datasets. The method employs a combination of rational cubic trigonometric functions within a zipper fractal framework, offering enhanced flexibility through shape parameters and scaling factors. Rigorous error analysis is presented to establish the convergence of the proposed zipper fractal interpolants to the underlying classical fractal functions, and subsequently, to the data-generating function. We derive necessary constraints on the scaling factors and shape parameters to ensure positivity preservation. By carefully selecting the signature, shape parameters, and scaling factors within these bounds, we construct a class of RCTZFIFs that effectively preserve the positive nature of the data, as compared to a reference interpolant that may violate this property. Numerical experiments and visualisations demonstrate the efficacy and robustness of our approach in preserving positivity while offering fractal flexibility.