• Detecting Chaotic Signals with...

Treffer: Detecting Chaotic Signals with Nonlinear Models

Title:
Detecting Chaotic Signals with Nonlinear Models
Authors:
Cai, Qin
Source:
Dissertations and Theses
Publisher Information:
PDXScholar
Publication Year:
1993
Collection:
Portland State University: PDXScholar
Subject Terms:
Signal processing -- Mathematical models, Chaotic behavior in systems -- Mathematical models, Electrical and Computer Engineering, Signal Processing
Document Type:
Fachzeitschrift text
File Description:
application/pdf
Language:
English
Relation:
https://pdxscholar.library.pdx.edu/open_access_etds/4564; https://pdxscholar.library.pdx.edu/context/open_access_etds/article/5635/viewcontent/Cai_Qin_1993.pdf
DOI:
10.15760/etd.6448
Availability:
https://pdxscholar.library.pdx.edu/open_access_etds/4564
https://doi.org/10.15760/etd.6448
https://pdxscholar.library.pdx.edu/context/open_access_etds/article/5635/viewcontent/Cai_Qin_1993.pdf
Rights:
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Accession Number:
edsbas.24D46381
Database:
BASE

Weitere Informationen

In this thesis we apply chaotic dynamic data analysis to the area of discrete time signal processing. A newly developed Hidden Filter Hidden Markov Model is introduced in detection of chaotic signals. Numerical experiments have verified that this novel nonlinear model outperforms linear AR model in detecting chaotic signals buried by noise having similar power spectra. A simple Histogram Model is proposed which can also be used to do detection on the data sets with chaotic behavior. Receiver Operating Characteristics for a variety of noise levels and model classes are reported.