PACS number(s): 02.70.Rr, 05.45.Tp, 05.45.PqAuthor name used in this publication: C. K. Tse2002-2003 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Modelling time series is quite a difficult task. The last recent years, reservoir computing approach...
An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single m...
Artificial neural networks (ANNs) are universal function approximators, therefore suitable to be tra...
Artificial neural networks (ANN) are typically composed of a large number of nonlinear functions (ne...
© Published under licence by IOP Publishing Ltd. Deep neural networks with a large number of paramet...
summary:Artificial neural networks (ANN) have received a great deal of attention in many fields of e...
Time series prediction is a very important technology in a wide variety of field. The actual time se...
Neural networks can be viewed as nonlinear models, where the weights are parameters to be estimated....
It has been demonstrated that in the realm of complex systems not only exact predic-tions of multiva...
Neural Network approaches to time series prediction are briefly discussed, and the need to specify a...
Neural network approaches to time series prediction are briefly discussed, and the need to specify a...
The main advantage of detecting chaos is that the time series is short term predictable. The predict...
An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single m...
This paper is concerned with modelling time series by single hidden-layer feedforward neural network...
In this thesis, we propose a recurrent FIR neural network, develop a constrained formulation for neu...
Modelling time series is quite a difficult task. The last recent years, reservoir computing approach...
An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single m...
Artificial neural networks (ANNs) are universal function approximators, therefore suitable to be tra...
Artificial neural networks (ANN) are typically composed of a large number of nonlinear functions (ne...
© Published under licence by IOP Publishing Ltd. Deep neural networks with a large number of paramet...
summary:Artificial neural networks (ANN) have received a great deal of attention in many fields of e...
Time series prediction is a very important technology in a wide variety of field. The actual time se...
Neural networks can be viewed as nonlinear models, where the weights are parameters to be estimated....
It has been demonstrated that in the realm of complex systems not only exact predic-tions of multiva...
Neural Network approaches to time series prediction are briefly discussed, and the need to specify a...
Neural network approaches to time series prediction are briefly discussed, and the need to specify a...
The main advantage of detecting chaos is that the time series is short term predictable. The predict...
An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single m...
This paper is concerned with modelling time series by single hidden-layer feedforward neural network...
In this thesis, we propose a recurrent FIR neural network, develop a constrained formulation for neu...
Modelling time series is quite a difficult task. The last recent years, reservoir computing approach...
An algorithm is introduced that trains a neural network to identify chaotic dynamics from a single m...
Artificial neural networks (ANNs) are universal function approximators, therefore suitable to be tra...
DOI
Add to ORKG