Creating artificial neural networks that generalize
Neural Networks
Bayesian methods for adaptive models
Bayesian methods for adaptive models
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Structural learning with forgetting
Neural Networks
Embedding as a modeling problem
Physica D
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Nonlinear Time Series Analysis
Nonlinear Time Series Analysis
Reconstructions and predictions of nonlinear dynamical systems: ahierarchical Bayesian approach
IEEE Transactions on Signal Processing
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
An information criterion for optimal neural network selection
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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In this work a novel approach for estimation of embedding parameters for reconstruction of underlying dynamical system from the observed nonlinear time series by a feedforward neural network with structural learning is proposed. The proposed scheme of optimal estimation of embedding parameters can be viewed as a global non-uniform embedding. It has been found that the proposed method is more efficient for estimating embedding parameters for reconstruction of the attractor in the phase space than conventional uniform embedding methods. The simulation has been done with Henon series and three other real benchmark data sets. The simulation results for short term prediction of Henon Series and the bench mark time series with the estimated embedding parameters also show that the estimated parameters with proposed technique are better than the estimated parameters with the conventional method in terms of the prediction accuracy. The proposed technique seems to be an efficient candidate for prediction of future values of noisy real world time series.