Document Type
Thesis
Date of Award
12-31-1990
Degree Name
Master of Science in Electrical Engineering - (M.S.)
Department
Electrical and Computer Engineering
First Advisor
Nirwan Ansari
Second Advisor
Edwin Hou
Third Advisor
Irving Y. Wang
Abstract
Stack filters are a class of sliding?window nonlinear digital filters that possess the weak superposition property(threshold decomposition) and the ordering property known as the stacking property. They have been demonstrated to be robust in suppressing noise. Two methods are introduced in this thesis to adaptively configure a stack filter. One is by employing the Least Mean Square(LMS) algorithm and the other is based on Perceptron learning.
Experimental results are presented to demonstrate the effectiveness of our methods to noise suppression.
Recommended Citation
Huang, Yu-chou, "Adaptive stack filtering by LMS and perceptron learning" (1990). Theses. 2432.
https://digitalcommons.njit.edu/theses/2432
