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Time series prediction by chaotic modeling of nonlinear dynamical systems

Arslan Basharat, Mubarak Shah
2009 2009 IEEE 12th International Conference on Computer Vision  
Observed time series from such a system can be embedded into a higher dimensional phase space without the knowledge of an exact model of the underlying dynamics.  ...  We use concepts from chaos theory in order to model nonlinear dynamical systems that exhibit deterministic behavior.  ...  Acknowledgements This research was funded in part by the US government VACE program.  ... 
doi:10.1109/iccv.2009.5459429 dblp:conf/iccv/BasharatS09 fatcat:x5lo2qulrfeg3f2zsl7dihauiy

A Prediction Method for Underwater Acoustic Chaotic Signal Based on RBF Neural Network

Guohui Li, Hong Yang
2014 Journal of Software  
In this paper, the chaotic time series RBF neural network model was designed.  ...  Typical Henon chaotic signal and the actual underwater acoustic chaotic signal are respectively predicted by the RBF neural network. Then the prediction results are analyzed.  ...  Chaotic signal is generated by deterministic nonlinear dynamical system. The prediction of chaotic signal is of great significance for the analysis and study of nonlinear dynamic system.  ... 
doi:10.4304/jsw.9.6.1581-1586 fatcat:txylhtm4qvdubhqpo3zobyzoye

CONTRIBUTIONS TO MODELING THE BEHAVIOR OF CHAOTIC SYSTEMS WITH APPLICABILITY IN ECONOMIC SYSTEMS

Cătălin DUMITRESCU
2019 Zenodo  
Keywords: Chaos theory, nonlinear dynamics, nonlinear time series analysis, chaos identification, Lyapunov exponent, neural networks prediction of chaotic time series, multilayer, neural networks of support  ...  The chaos theory aims to explain and to predict in a short time the seemingly random and unpredictable behavior of the systems Nonlinear.  ...  ; • Identification of the optimal order of the neural model used for modeling and prediction of chaotic time series.  ... 
doi:10.5281/zenodo.3592373 fatcat:2kciyib2mjgjhes7zlweqm7uru

Introduction to Controversial Topics in Nonlinear Science: Is the Normal Heart Rate Chaotic?

Leon Glass
2009 Chaos  
In June 2008, the editors of Chaos decided to institute a new section to appear from time to time that addresses timely and controversial topics related to nonlinear science.  ...  The first of these deals with the dynamical characterization of human heart rate variability. We asked authors to respond to the following questions: Is the normal heart rate chaotic?  ...  ., "Prediction in chaotic nonlinear systems: Methods for time series with broadband Fourier spec- Downloaded 06 Dec 2009 to 132.216.138.7.  ... 
doi:10.1063/1.3156832 pmid:19566276 fatcat:blgm3osgmvhqpliokuy4scybgy

Incomplete Phase Space Reconstruction Method Based on Subspace Adaptive Evolution Approximation

Tai-fu Li, Wei Jia, Wei Zhou, Ji-ke Ge, Yu-cheng Liu, Li-zhong Yao
2013 Journal of Applied Mathematics  
The chaotic time series can be expanded to the multidimensional space by phase space reconstruction, in order to reconstruct the dynamic characteristics of the original system.  ...  The chaotic time series prediction based on the phase space reconstruction can be considered as the subspace approximation problem in different neighborhood at different time.  ...  Acknowledgments This work was supported by the National Science Foundation of China (no. 51075418), the National Science Foundation of China (no. 61174015), Chongqing CMEC Foundations of China (no.  ... 
doi:10.1155/2013/983051 fatcat:qshsjxf2svhldh3z6ofancx6cy

A Multistep Chaotic Model for Municipal Solid Waste Generation Prediction

Jingwei Song, Jiaying He
2014 Environmental Engineering Science  
Moreover, this chaotic model was less time consuming than ANN and PLS-SVM models.  ...  For MSW generation prediction with long history data, this forecasting model was created based on a nonlinear dynamic method called phase-space reconstruction.  ...  (a), (c), (e), and (g) are the original time series and predicted time series by chaotic model, NARX, PLS-SVM, and sARIMA.  ... 
doi:10.1089/ees.2014.0031 pmid:25125942 pmcid:PMC4118706 fatcat:3cb43atwfvd63kr6s2dn7df2qq

Chaotic Vibration Prediction of a Free-Floating Flexible Redundant Space Manipulator

Congqing Wang, Linfeng Wu
2016 Shock and Vibration  
The one-step prediction model for the chaotic time series and the chaotic vibration was established by using support vector regression (SVR) prediction model with RBF kernel function.  ...  Then, the chaotic dynamic behavior of the manipulator is analyzed by chaotic numerical methods, in which time series, phase plane portrait, Poincaré map, and Lyapunov exponents are used to analyze the  ...  Acknowledgments This work was supported by the Foundation of Manufacturing Systems Engineering State Key Laboratory, Xi'an Jiaotong University of China (no. 201002), and the Jiangsu Province Science and  ... 
doi:10.1155/2016/6015275 fatcat:u3zlzfpvpvce3awb6dw7ohp5pm

Forecasting Agricultural Production: A Chaotic Dynamic Approach

Bunyamin Demir, Nesrin Alptekin, Yilmaz Kilicaslan, Mehmet Ergen, Nilgun Caglarirmak Uslu
2015 World Journal of Applied Economics  
Our dynamic system constructed predicted the supply of year 2010 with % 0.5 error for wheat, %5 error for barley, and %2.5 error ratio for rice.  ...  The aim of this study is to examine the existence of chaotic structure in agricultural production in Turkey by using Chaotic Dynamic Analysis (CDA) and to provide accurate forecasts of agricultural production  ...  This study, by proposing a new model of predication of the future values of agricultural supply, will help to produce effective policies to prevent supply disequilibrium, and excess price fluctuations.  ... 
doi:10.22440/econworld.j.2015.1.1.bd.0007 fatcat:s7v47wnxdzddhn4t2pdjnmqhmu

Power Forecasting of Combined Heating and Cooling Systems Based on Chaotic Time Series

Liu Hai, Song Yong, Du Qingfu
2015 Journal of Control Science and Engineering  
A predicator of the output power of the distributed generation system is to establish a nonlinear model of the dynamic system based on real time series in the reconstructed phase space.  ...  Chaos is an inherent property of nonlinear dynamic system.  ...  The prediction of chaotic time series is to establish a nonlinear model of the dynamic system based on real time series in the reconstructed phase space.  ... 
doi:10.1155/2015/174203 fatcat:5wpznp6rpnak7bsftd54amfb2i

Nonlinear Test and Forecasting of Petroleum Futures Prices Time Series

Liu Lixia
2011 Energy Procedia  
The results indicate the appropriateness of the nonlinear dynamical approach for characterizing and predicting the dynamics of petroleum futures prices.  ...  Nonlinear forecast modeling based on phase space reconstruction is applied to petroleum futures prices series.  ...  Acknowledgements We are thankful and acknowledge the support under research Fund for the Doctoral Program of Higher Education of China (20090032110031).  ... 
doi:10.1016/j.egypro.2011.03.132 fatcat:35sk7cbrpvbfdawimbolqjddtu

Detecting Predictable Segments of Chaotic Financial Time Series via Neural Network

Tianle Zhou, Chaoyi Chu, Chaobin Xu, Weihao Liu, Hao Yu
2020 Electronics  
However, it is the high volatility and chaotic dynamics of financial time series that constitute the most significantly influential factors affecting the prediction effect.  ...  To summarize, the model has proven itself able to mark the unpredictable time series area and evaluate the unpredictable risk by using 1-dimension time series data.  ...  of chaotic dynamical systems.  ... 
doi:10.3390/electronics9050823 fatcat:fyo4tbzcrjg7jntos4la26qg34

Application of particle swarm optimization with ANFIS model for double scroll chaotic system

W. A. Wali
2021 International Journal of Power Electronics and Drive Systems (IJPEDS)  
This paper shows how the chaotic time series prediction is difficult and distort even if Neuro-Fuzzy such as Adaptive Neural Fuzzy Inference System (ANFIS) is used under any disturbance.  ...  Changes in the bias of the nonlinear resistor were used as a disturbance. The predicted chaotic data is compared with data from the chaotic circuit.  ...  INTRODUCTION Nonlinear prediction of chaotic time series is very challenging in the prediction area [1] .  ... 
doi:10.11591/ijece.v11i1.pp328-335 fatcat:t7lzawqvhzdbvc3brke7qh4lqi

Modeling nonlinear dynamics of circulating fluidized beds using neural networks

Wei Chen, Atsushi Tsutsumi, Haiyan Lin, Kentaro Otawara
2005 China PARTICUOLOGY  
Acknowledgements The authors would like to thank the financial support of a "Core Research for Evolutional Science and Technology" grant from the Japan Science and Technology Agency (JST).  ...  the certain nonlinear dynamic properties of the real system.  ...  of chaotic system.  ... 
doi:10.1016/s1672-2515(07)60172-9 fatcat:axpt2gg4sfeafjvui4kvw4tigy

Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study

Ali Gholipour, Babak N. Araabi, Caro Lucas
2006 Neural Processing Letters  
In this study, several neural and neurofuzzy models with different learning algorithms are examined for prediction of several benchmark chaotic systems and time series.  ...  The prediction accuracy and generalization ability of neural/neurofuzzy models for chaotic time series prediction highly depends on employed network model as well as learning algorithm.  ...  Acknowledgments The authors are thankful to the World Data Center for Geomagnetism and Space Magnetism, Kyoto University, which provided the access to the data files of AE index.  ... 
doi:10.1007/s11063-006-9021-x fatcat:zq2nylmztnbzderjyfvy2qzqw4

Model-free prediction of noisy chaotic time series by deep learning [article]

Kyongmin Yeo
2017 arXiv   pre-print
We present a deep neural network for a model-free prediction of a chaotic dynamical system from noisy observations.  ...  The LSTM model is trained by minimizing a regularized cross-entropy function. The LSTM model is validated against delay-time chaotic dynamical systems, Mackey-Glass and Ikeda equations.  ...  We present a deep neural network for a model-free prediction of a chaotic dynamical system from noisy observations.  ... 
arXiv:1710.01693v1 fatcat:esv2qpp7wfaetfqjn5xyixc2oq
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