Model Development of Analogue Wavelet Transform Based the Predicted Imaginary Part of the Dynamics of Rational Map Having Zeros

The dynamics of quadratic polynomials have already gotten a lot of attention. The dynamics of rational functions and their properties are equally interesting. In this chapter, we consider the solution of the classical Yang-Mills equation by Witten [1] and generate computer images from a C++ computer program. We then use predictive modelling software to create an artificial neural network model based on RMS kind of error from two samples of points extracted from the generated images. By inputting the real parts of sample I and sample II [2] to the artificial neural network, the imaginary component of sample II could be predicted. In forecasting the imaginary component of sample II, the real part of sample II is more important than the actual part of sample I. Sample II's projected imaginary component is then imported into Matlab Signal Processing Tool (SPTool) via Matlab workspace. We employ a stable band pass filter to the model to remove noise from it for its analysis. A modulated signal is produced, revealing that the methodology can be used to investigate the properties of the computer generated images derived from the generated wavelet. We then import the predicted imaginary part of sample II into autoSIGNAL software for time-frequency analysis of the continuous wavelet transform. This study attempts to bridge the gap between the dynamics of rational maps and the dynamics of electric the power system [3,4].