Modeling a fractal-based C Beizer function for predicting underlying data pattern

Abstract

As artificial intelligence advances, more and more tasks that formerly required human discretion can be automated. The proposed study aims to create an automated supervised, hybrid computing algorithm for the synthesis and analysis of engineering and scientific data and gaining insightful knowledge about the data. A novel iterated function approach has been designed by integrating rational C Bezier function with classical fractal function. Sufficient conditions on the scaling and shape factors have been calculated to obtain various simulating patterns occurring in data. The developed model is trained and tested on a set of data values to predict prevalent shape characteristics of the data. Numerical examples validate the suggested approach.