Author Type

Graduate Student

Date of Award

1998

Document Type

Thesis

Publication Status

Version of Record

Submission Date

October 2026

Thesis/Dissertation Advisor [Chair]

Zilouchian, Ali

Abstract

In this dissertation, identification and approximation of one-dimensional (1-D) and two-dimensional (2-D) recursive digital filters are addressed. In the identification phase, a novel Neural Network (NN) structure is proposed which provides the state-space model of 1-D filters based upon input-output data. The state space identification technique is also extended to 2-D digital filters and several comparison studies are performed. In the approximation phase, frequency-domain balanced structures for 1-D as well as 2-D digital filters are proposed. The model reduction technique is based on the conceptual view point of balancing the controllability and observability Grammians of a digital filter in an arbitrary frequency range of operation. Finally, the interrelations between these two phases are presented. Extensive simulation experiments are presented to demonstrate the effectiveness of proposed methods.

Extent

197 p.

Comments

College of Engineering and Computer Science|FAU Electronic Theses and Dissertations Collection|Thesis (Ph.D.)--Florida Atlantic University, 1998.|College of Engineering and Computer Science

Identifier

FADT12555

Format

pdf

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