The New Jersey Institute of Technology's
Electronic Theses & Dissertations Project

Title:	Adaptive stack filtering by LMS and perceptron learning
Author:	Huang, Yu-chou
View Online:	njit-etd1990-058 ( [vi], 60 pages ~ 1.1 MB pdf)
Department:	Department of Electrical and Computer Engineering
Degree:	Master of Science
Program:	Electrical Engineering
Document Type:	Thesis
Advisory Committee:	Ansari, Nirwan (Committee chair) Hou, Edwin (Committee member) Wang, Irving Y. (Committee member)
Date:	1990-12
Keywords:	Adaptive filters Perceptrons Random noise theory
Availability:	Unrestricted
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.