State estimation of a chaotic Galton board model

Abstract

In this study, the application of the gradient descent of indeterminism (GDI) shadowing filter to a recently proposed chaotic Galton board model is presented and discussed. It is found that the original GDI shadowing filter does not provide stability to the convergence of the indeterminism and the error. The adaptive step-size (AS) is introduced to address the problem. The advantages of AS over fixed step-size (FS) and how it can improve the original shadowing filter are investigated in this study. The AS will guarantee successful implementation of GDI shadowing algorithm and will improve the performance of the original shadowing filter.