Shape preservation of scientific data through rational fractal splines
Fractal interpolation is a modern technique in approximation theory to fit and analyze scientific data. We develop a new class of \(\mathcal C ^1\)- rational cubic fractal interpolation functions, where the associated iterated function system uses rational functions of the form \(\frac{p_i(x)}{q_i(x)},\) where \(p_i(x)\) and \(q_i(x)\) are cubic polynomials involving two shape parameters. The rational cubic iterated function system scheme provides an additional freedom over the classical rational cubic interpolants due to the presence of the scaling factors and shape parameters. The classical rational cubic functions are obtained as a special case of the developed fractal interpolants. An upper bound of the uniform error of the rational cubic fractal interpolation function with an original function in \(\mathcal C ^2\) is deduced for the convergence results. The rational fractal scheme is computationally economical, very much local, moderately local or global depending on the scaling factors and shape parameters. Appropriate restrictions on the scaling factors and shape parameters give sufficient conditions for a shape preserving rational cubic fractal interpolation function so that it is monotonic, positive, and convex if the data set is monotonic, positive, and convex, respectively. A visual illustration of the shape preserving fractal curves is provided to support our theoretical results.
fractals fractal interpolation functions iterated function systems rational cubic interpolation monotonicity positivity convexity rational cubic fractal functions 28a80 41a20 65d10 41a25 65d05 65d07 26a48 26a51