Configuring radial basis function network using fractal scaling process with application to chaotic time series prediction

Omidvar, Abbas Erfanian

doi:10.1016/j.chaos.2004.03.008

Cited by 16 publications

(2 citation statements)

References 14 publications

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“…These signals are obtained from a hot wire anemometer at a single point inside the cylinder to detect coherent structures. In [34], the authors presented the RBFNN model for forecasting the time series of the logistic map, Henon map, Mackey-Glass, and Duffing's systems. In [36], the researchers developed the recurrent predictor neural network model for predicting the annual and monthly sunspot time series.…”

Section: Input Layer ∈ Rmentioning

confidence: 99%

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Ramadevi

Bingi

2022

Symmetry

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Traditional statistical, physical, and correlation models for chaotic time series prediction have problems, such as low forecasting accuracy, computational time, and difficulty determining the neural network’s topologies. Over a decade, various researchers have been working with these issues; however, it remains a challenge. Therefore, this review paper presents a comprehensive review of significant research conducted on various approaches for chaotic time series forecasting, using machine learning techniques such as convolutional neural network (CNN), wavelet neural network (WNN), fuzzy neural network (FNN), and long short-term memory (LSTM) in the nonlinear systems aforementioned above. The paper also aims to provide issues of individual forecasting approaches for better understanding and up-to-date knowledge for chaotic time series forecasting. The comprehensive review table summarizes the works closely associated with the mentioned issues. It includes published year, research country, forecasting approach, application, forecasting parameters, performance measures, and collected data area in this sector. Future improvements and current studies in this field are broadly examined. In addition, possible future scopes and limitations are closely discussed.

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Section: Input Layer ∈ Rmentioning

confidence: 99%

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Ramadevi

Bingi

2022

Symmetry

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“…[4,5] Nevertheless, in recent years, various innovative approaches, such as time-delay embedding, [6,7] wavelets, [8] and global dynamical models, [9] have been proposed to resolve the problems associated with the nonlinear time series. On the other hand, more recent studies place more emphasis on the general neural networks, [10−12] the radial basis function network, [13,14] the Gaussian process, [15] and the kennel methods such as support vector machines (SVM). [16−18] In addition, Grau-Sánchez et al analyzed the chaotic behavior of a dis-crete dynamical system based on a uniparametric family of real functions.…”

Section: Introductionmentioning

confidence: 99%

Chaotic behavior learning of Chua's circuit

Sun¹

2012

Chinese Phys. B

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Least-square support vector machines (LS-SVM) are applied for learning the chaotic behavior of Chua's circuit. The system is divided into three multiple-input single-output (MISO) structures and the LS-SVM are trained individually. Comparing with classical approaches, the proposed one reduces the structural complexity and the selection of parameters is avoided. Some parameters of the attractor are used to compare the chaotic behavior of the reconstructed and the original systems for model validation. Results show that the LS-SVM combined with the MISO can be trained to identify the underlying link among Chua's circuit state variables, and exhibit the chaotic attractors under the autonomous working mode.

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Self-Organizing Polynomial Neural Network for Forecasting Chaotic Time Series: Comparison Results

2011

Communications in Computer and Information Science

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Configuring radial basis function network using fractal scaling process with application to chaotic time series prediction

Cited by 16 publications

References 14 publications

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review

Chaotic behavior learning of Chua's circuit

Self-Organizing Polynomial Neural Network for Forecasting Chaotic Time Series: Comparison Results

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