System identification: theory for the user
System identification: theory for the user
Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
Multilayer feedforward networks are universal approximators
Neural Networks
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Universal approximation using radial-basis-function networks
Neural Computation
Neural networks and the bias/variance dilemma
Neural Computation
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Solving ordinary differential equations I (2nd revised. ed.): nonstiff problems
Approximation and radial-basis-function networks
Neural Computation
Nonlinear black-box models in system identification: mathematical foundations
Automatica (Journal of IFAC) - Special issue on trends in system identification
Bias/variance decompositions for likelihood-based estimators
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Universal approximation bounds for superpositions of a sigmoidal function
IEEE Transactions on Information Theory
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Gradient methods for the optimization of dynamical systems containing neural networks
IEEE Transactions on Neural Networks
Comparison of four neural net learning methods for dynamic system identification
IEEE Transactions on Neural Networks
Chaotic dynamic characteristics in swarm intelligence
Applied Soft Computing
Time-series prediction with single integrate-and-fire neuron
Applied Soft Computing
Modeling and prediction with a class of time delay dynamic neural networks
Applied Soft Computing
Recurrent neural network based BER prediction for NLOS channels
Mobility '07 Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology
A Hybrid Model of Partial Least Squares and RBF Neural Networks for System Identification
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
WSEAS Transactions on Information Science and Applications
Novel FTLRNN with gamma memory for short-term and long-term predictions of chaotic time series
Applied Computational Intelligence and Soft Computing
Applied Computational Intelligence and Soft Computing
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This paper investigates the prediction of a Lorenz chaotic attractor having relatively high values of Lypunov's exponents. The characteristic of this time series is its rich chaotic behavior. For such dynamic reconstruction problem, regularized radial basis function (RBF) neural network (NN) models have been widely employed in the literature. However, author recommends using a two-layer multi-layer perceptron (MLP) NN-based recurrent model. When none of the available linear models have been able to learn the dynamics of this attractor, it is shown that the proposed NN-based auto regressive (AR) and auto regressive moving average (ARMA) models with regularization have not only learned the true trajectory of this attractor, but also performed much better in multi-step-ahead predictions. However, equivalent linear models seem to fail miserably in learning the dynamics of the time series, despite the low values of Akaike's final prediction error (FPE) estimate. Author proposes to employ the recurrent NN-based ARMA model with regularization which clearly outperforms all other models and thus, it is possible to obtain good results for prediction and reconstruction of the dynamics of the chaotic time series with NN-based models.