Classification of the Most Commonly Used Adaptive Algorithms and Their Extension to Multidimensional Variable Step Size Sequential Adaptive Algorithms
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.
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Master of Science (M.S.)
College of Engineering
Length of Campus-only Access
Masters Thesis (Open Access)
Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic
Ghosh, Shomit M., "Classification of the Most Commonly Used Adaptive Algorithms and Their Extension to Multidimensional Variable Step Size Sequential Adaptive Algorithms" (1990). Retrospective Theses and Dissertations. 3989.