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

Title:	Simplification of the generalized adaptive neural filter and comparative studies with other nonlinear filters
Author:	Hanek, Henry Steven
View Online:	njit-etd1993-091 (xii, 113 pages ~ 5.5 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) Siveski, Zoran (Committee member) Hou, Edwin (Committee member)
Date:	1993-10
Keywords:	Adaptive filters Image processing
Availability:	Unrestricted
Abstract:	Recently, a new class of adaptive filters called Generalized Adaptive Neural Filters (GANFs) has emerged. They share many characteristics in common with stack filters, include all stack filters as a subset. The GANFs allow a very efficient hardware implementation once they are trained. However, there are some problems associated with GANFs. Three of these arc slow training speeds and the difficulty in choosing a filter structure and neural operator. This thesis begins with a tutorial on filtering and traces the GANF development up through its origin -- the stack filter. After the GANF is covered in reasonable depth, its use as an image processing filter is examined. Its usefulness is determined based on simulation comparisons with other common filters. Also, some problems of GANFs are looked into. A brief study which investigates different types of neural networks and their applicability to GANFs is presented. Finally, some ideas on increasing the speed of the GANF are discussed. While these improvements do not completely solve the GANF's problems, they make a measurable difference and bring the filter closer to reality.