1991
DOI: 10.1007/bf01940473
|View full text |Cite
|
Sign up to set email alerts
|

Model reduction of 2-D systems via orthogonal series

Abstract: Abstract. In this article, the problem of model reduction of 2-D systems is studied via orthogonal series. The algorithm proposed reduces the problem to an overdetermined linear algebraic system of equations, which may readily be solved to yield the simplified model. When this model approximates adequately the original system, it has many important advantages, e.g., it simplifies the analysis and simulation of the original system, it reduces the computational effort in design procedures, it reduces the hardwar… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
1
0

Year Published

1997
1997
2023
2023

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 18 publications
0
1
0
Order By: Relevance
“…Therefore, for most of these applications, the designed IIR filters should have a nearly linear-phase response. Moreover, hard constraints on the location of the poles of IIR filters must be enforced to ensure the stability of the obtained filters [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. These and other design requirements usually lead to highly nonlinear constrained optimization problems that require highly sophisticated optimization methods [1,12,[29][30][31][32].…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, for most of these applications, the designed IIR filters should have a nearly linear-phase response. Moreover, hard constraints on the location of the poles of IIR filters must be enforced to ensure the stability of the obtained filters [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. These and other design requirements usually lead to highly nonlinear constrained optimization problems that require highly sophisticated optimization methods [1,12,[29][30][31][32].…”
Section: Introductionmentioning
confidence: 99%
“…Varoufakis [19] applied the continued-fraction expansion procedure to derive a Pade-type approximants for 2-D systems. In [ 17], the simplified models for 2-D systems are derived via the orthogonal series expansion. The advantage of extending these 1-D model reduction techniques to 2-D systems is computational simplicity.…”
Section: Introductionmentioning
confidence: 99%