Prediction of chaotic time series using neural network

Authors:
Hamid Yazdani
Affiliations:
Semnan Univsesity, Iran
Venue:
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Year:
2009

Citing 7
Cited 1

Predicting chaotic time series with wavelet networks

Physica D
Nonlinear modelling and prediction with feedforward and recurrent networks

Physica D
Practical method for determining the minimum embedding dimension of a scalar time series

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

Neural Processing Letters
Chaotic Time Series Prediction Based on Radial Basis Function Network

SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 01
Regularization in the selection of radial basis function centers

Neural Computation
Predicting chaotic time series by boosted recurrent neural networks

ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II

Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a novel algorithm for determining the structure of a radial basis function (RBF) network (the number of hidden units) while it is used for dynamic modeling of chaotic time series. It can be seen that the hidden units in the RBF network can form hyperplanes to partition the input space into various regions in each of which it is possible to approximate the dynamics with a basis function. The number of regions corresponds to the number of hidden units. Accordingly, the network topology is efficiently determined based on the complexity of the underlying dynamics as reflected in the observed time series. The feasibility of the proposed scheme is examined through dynamic modeling of the well-known chaotic time series. The results show that the new method can improve the predictability of chaotic time series with a suitable number of hidden units compared to that of reported in the literature.