Classification of the Most Commonly Used Adaptive Algorithms and Their Extension to Multidimensional Variable Step Size Sequential Adaptive Algorithms

Abstract

In this thesis it is shown that the commonly used adaptive algorithms are closely related to each other and can be derived from one another. Progressing from one algorithm to the next, the gradual transition in the tradeoff between the computational complexity, the length of the processed data record, and adaptation performance, such as speed and accuracy, is demonstrated.Comparative discussions supported with computer simulation results are given. In the second part of the optimality criterion governing the choice of the convergence factor in the case of two-dimensional variable step size sequential algorithms is extended from the one-dimensional case. The Two-Dimensional Individual Adaptation (TDIA) and the Two-Dimensional Homogeneous Adaptation (TDHA) algorithms are proposed and investigated. The performance of these algorithms for the two-dimensional system identification mode is studied using computer simulations. It is shown that these two algorithms can be successfully applied to adaptive noise-cancellation in two-dimensional signals like images.

Notes

This item is only available in print in the UCF Libraries. If this is your thesis or dissertation, you can help us make it available online for use by researchers around the world by STARS for more information.

Graduation Date

1990

Semester

Fall

Advisor

Mikhael, Wasfy

Degree

Master of Science (M.S.)

College

College of Engineering

Department

Electrical Engineering

Format

PDF

Pages

115 p.

Language

English

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Identifier

DP0027275

Subjects

Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic

Accessibility Status

Searchable text

This document is currently not available here.

Share

COinS