Latex/Masterarbeit
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

2.2

Browse Source
This commit is contained in:
Patrick Hangl
2025-09-30 17:01:58 +02:00
parent c48bf258b1
commit 1e1297f440
1 changed files with 2 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
chapter_02.tex
View File

@@ -52,8 +52,9 @@ Examples for an adaptive filter is the Least-Mean-Square-Algorithm used for adap
\subsection{Finite Impulse Response- and Infinite Impulse Response-filters}
Before we continue with the introduction to the actual topic of this thesis, ANR, two very essential filter designs need further explanation - the Finite Impulse Response- (FIR) and Infinite Impulse Response-filters (IIR).
\subsubsection{Finite Impulse Response-filters}
A Finite Impulse Respone Filter, commonly referred to as a ''Feedforward Filter'' is defined through the property, that it uses only present and past input values and not feedback from output samples - therefore the response of a FIR-filter reaches zero after a finite number of samples. Due to the fact, that there is no feedback, a FIR-filter offers unconditional stability, meaning that the filter response can´t diverge in any case. A disatavante to the FIR-filter desgin is the relatively slow frequency reaction compared to its IIR counterpart.
\subsubsection{Infinite Impulse Response-filters}
A Ininite Impulse Respone Filter, commonly referred to as a ''Feedback Filter'' does, in contrary to its FIR-counterpart, use past output samples in addition to current and past input samples - hterefore the response of a IIR-filter theoretically continues indefinitely.
\subsection{Introduction to Adaptive Nose Reduction}