Phase Space Reconstruction of Chaotic Time Series Using an Intelligent Method
In the face of a chaotic system whose mathematical model is not available, because of unknown effective factors and unavailable dynamical equations, using time series approach can be useful. Therefore, phase space reconstruction of a chaotic system by using a scalar time series from its output obser...
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Najafabad Branch, Islamic Azad University
2010-10-01
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doaj-7e082fb87a3c4792a44b7f9407547e2b2020-11-25T00:35:56ZengNajafabad Branch, Islamic Azad UniversityJournal of Intelligent Procedures in Electrical Technology2322-38712345-55942010-10-0113310Phase Space Reconstruction of Chaotic Time Series Using an Intelligent MethodMaryam Pari Zangeneh0Mohammad Ataei1Peiman Moallem2Najafabad Branch, Islamic Azad UniversityUniversity of IsfahanUniversity of IsfahanIn the face of a chaotic system whose mathematical model is not available, because of unknown effective factors and unavailable dynamical equations, using time series approach can be useful. Therefore, phase space reconstruction of a chaotic system by using a scalar time series from its output observations is considered for obtaining information on this system from its one-dimensional signal. Two parameters Delay time and Embedding dimension are needed for phase space reconstruction based on embedding theorem. In this paper a method for estimation of an appropriate embedding dimension of underlying chaotic system from the observed time series by using Time Delay Neural Network (TDNN) is presented. This new way is different from the conventional False Nearest Neighbors (FNN) method. The embedding dimension estimations have been compared with FNN method and their comparison shows the effectiveness of the proposed methodology.http://jipet.iaun.ac.ir/pdf_4461_6677b643bade8836fa9ddfffc99bc2f0.htmlEmbedding dimensionFalse nearest neighborsChaotic time seriesFocused time delay neural network |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Maryam Pari Zangeneh Mohammad Ataei Peiman Moallem |
spellingShingle |
Maryam Pari Zangeneh Mohammad Ataei Peiman Moallem Phase Space Reconstruction of Chaotic Time Series Using an Intelligent Method Journal of Intelligent Procedures in Electrical Technology Embedding dimension False nearest neighbors Chaotic time series Focused time delay neural network |
author_facet |
Maryam Pari Zangeneh Mohammad Ataei Peiman Moallem |
author_sort |
Maryam Pari Zangeneh |
title |
Phase Space Reconstruction of Chaotic Time Series Using an Intelligent Method |
title_short |
Phase Space Reconstruction of Chaotic Time Series Using an Intelligent Method |
title_full |
Phase Space Reconstruction of Chaotic Time Series Using an Intelligent Method |
title_fullStr |
Phase Space Reconstruction of Chaotic Time Series Using an Intelligent Method |
title_full_unstemmed |
Phase Space Reconstruction of Chaotic Time Series Using an Intelligent Method |
title_sort |
phase space reconstruction of chaotic time series using an intelligent method |
publisher |
Najafabad Branch, Islamic Azad University |
series |
Journal of Intelligent Procedures in Electrical Technology |
issn |
2322-3871 2345-5594 |
publishDate |
2010-10-01 |
description |
In the face of a chaotic system whose mathematical model is not available, because of unknown effective factors and unavailable dynamical equations, using time series approach can be useful. Therefore, phase space reconstruction of a chaotic system by using a scalar time series from its output observations is considered for obtaining information on this system from its one-dimensional signal. Two parameters Delay time and Embedding dimension are needed for phase space reconstruction based on embedding theorem. In this paper a method for estimation of an appropriate embedding dimension of underlying chaotic system from the observed time series by using Time Delay Neural Network (TDNN) is presented. This new way is different from the conventional False Nearest Neighbors (FNN) method. The embedding dimension estimations have been compared with FNN method and their comparison shows the effectiveness of the proposed methodology. |
topic |
Embedding dimension False nearest neighbors Chaotic time series Focused time delay neural network |
url |
http://jipet.iaun.ac.ir/pdf_4461_6677b643bade8836fa9ddfffc99bc2f0.html |
work_keys_str_mv |
AT maryamparizangeneh phasespacereconstructionofchaotictimeseriesusinganintelligentmethod AT mohammadataei phasespacereconstructionofchaotictimeseriesusinganintelligentmethod AT peimanmoallem phasespacereconstructionofchaotictimeseriesusinganintelligentmethod |
_version_ |
1725307012190830592 |