Neural Network based Modeling, Characterization and Identification of Chaotic Systems in Nature
Modeling of chaotic systems, based on the output time series, is quite promising since the output often represents the characteristic behaviour of the total system. It has been an interesting topic for researchers over the past few years. So far, some methods are developed for the identification of...
Main Authors: | , , |
---|---|
Format: | Thesis |
Language: | English |
Published: |
Cochin University of Science and Technology
2015
|
Subjects: | |
Online Access: | http://dyuthi.cusat.ac.in/purl/5162 |
id |
ftcochinuniv:oai:dyuthi.cusat.ac.in:purl/5162 |
---|---|
record_format |
openpolar |
spelling |
ftcochinuniv:oai:dyuthi.cusat.ac.in:purl/5162 2023-05-15T17:36:30+02:00 Neural Network based Modeling, Characterization and Identification of Chaotic Systems in Nature Archana, R Dr. R Gopikakumari Dr. A Unnikrishnan 2015-03-12 http://dyuthi.cusat.ac.in/purl/5162 en eng Cochin University of Science and Technology http://dyuthi.cusat.ac.in/purl/5162 Neural networks Chaotic systems Chaotic weather systems Sunspot time series System modeling Simulation Thesis 2015 ftcochinuniv 2023-03-20T19:09:04Z Modeling of chaotic systems, based on the output time series, is quite promising since the output often represents the characteristic behaviour of the total system. It has been an interesting topic for researchers over the past few years. So far, some methods are developed for the identification of chaotic systems. Because of the intense complexity of chaotic systems, the performance of existing algorithms is not always satisfactory. Application of chaotic system theory to socially relevant problems like environmental studies is the need of the hour Neural networks have the required self-learning capability to tune the network parameters (i.e. weights) for identifying highly non-linear and chaotic systems. In the present work, effectiveness of modeling a chaotic system using dynamic neural networks has been demonstrated. From the rich literature available for non-linear modeling with neural networks, the Recurrent Neural Network (RNN) structure is selected. The Extended Kalman Filter (EKF) algorithm is used to train the RNN. Further, the Expectation Maximization algorithm is used to effectively arrive at the initial states and the state covariance. Particle filter algorithm with its two important variants namely Sampling Importance Resampling (SIR) and Rao Blackwellised algorithms are also used for training the given RNN. Four standard chaotic systems, Lorenz, Rossler, Chua and Chen, are modelled with the three algorithms. The best algorithm is found to be EKF-EM based on the least mean square error criterion. Validation of RNN model with EKFEM algorithm is done in time domain by Estimation of embedding dimension, Phase plots, Lyapunov Exponents, Kaplan -Yorke dimension and Bifurcation diagrams. Analysis of the chaotic systems is also performed in the transform domain using Fourier, Wavelet and Mapped Real Transforms. viii Natural chaotic systems are analyzed based on the selected model structure and training algorithm, taken for analysis. Sunspot, Venice Lagoon and North Atlantic oscillations are the three of ... Thesis North Atlantic Dyuthi - Digital Repository of Cochin University of Science and Technology (India) |
institution |
Open Polar |
collection |
Dyuthi - Digital Repository of Cochin University of Science and Technology (India) |
op_collection_id |
ftcochinuniv |
language |
English |
topic |
Neural networks Chaotic systems Chaotic weather systems Sunspot time series System modeling Simulation |
spellingShingle |
Neural networks Chaotic systems Chaotic weather systems Sunspot time series System modeling Simulation Archana, R Dr. R Gopikakumari Dr. A Unnikrishnan Neural Network based Modeling, Characterization and Identification of Chaotic Systems in Nature |
topic_facet |
Neural networks Chaotic systems Chaotic weather systems Sunspot time series System modeling Simulation |
description |
Modeling of chaotic systems, based on the output time series, is quite promising since the output often represents the characteristic behaviour of the total system. It has been an interesting topic for researchers over the past few years. So far, some methods are developed for the identification of chaotic systems. Because of the intense complexity of chaotic systems, the performance of existing algorithms is not always satisfactory. Application of chaotic system theory to socially relevant problems like environmental studies is the need of the hour Neural networks have the required self-learning capability to tune the network parameters (i.e. weights) for identifying highly non-linear and chaotic systems. In the present work, effectiveness of modeling a chaotic system using dynamic neural networks has been demonstrated. From the rich literature available for non-linear modeling with neural networks, the Recurrent Neural Network (RNN) structure is selected. The Extended Kalman Filter (EKF) algorithm is used to train the RNN. Further, the Expectation Maximization algorithm is used to effectively arrive at the initial states and the state covariance. Particle filter algorithm with its two important variants namely Sampling Importance Resampling (SIR) and Rao Blackwellised algorithms are also used for training the given RNN. Four standard chaotic systems, Lorenz, Rossler, Chua and Chen, are modelled with the three algorithms. The best algorithm is found to be EKF-EM based on the least mean square error criterion. Validation of RNN model with EKFEM algorithm is done in time domain by Estimation of embedding dimension, Phase plots, Lyapunov Exponents, Kaplan -Yorke dimension and Bifurcation diagrams. Analysis of the chaotic systems is also performed in the transform domain using Fourier, Wavelet and Mapped Real Transforms. viii Natural chaotic systems are analyzed based on the selected model structure and training algorithm, taken for analysis. Sunspot, Venice Lagoon and North Atlantic oscillations are the three of ... |
format |
Thesis |
author |
Archana, R Dr. R Gopikakumari Dr. A Unnikrishnan |
author_facet |
Archana, R Dr. R Gopikakumari Dr. A Unnikrishnan |
author_sort |
Archana, R |
title |
Neural Network based Modeling, Characterization and Identification of Chaotic Systems in Nature |
title_short |
Neural Network based Modeling, Characterization and Identification of Chaotic Systems in Nature |
title_full |
Neural Network based Modeling, Characterization and Identification of Chaotic Systems in Nature |
title_fullStr |
Neural Network based Modeling, Characterization and Identification of Chaotic Systems in Nature |
title_full_unstemmed |
Neural Network based Modeling, Characterization and Identification of Chaotic Systems in Nature |
title_sort |
neural network based modeling, characterization and identification of chaotic systems in nature |
publisher |
Cochin University of Science and Technology |
publishDate |
2015 |
url |
http://dyuthi.cusat.ac.in/purl/5162 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_relation |
http://dyuthi.cusat.ac.in/purl/5162 |
_version_ |
1766135998526259200 |