Online sequential echo state network with sparse RLS algorithm for time series prediction.
Neural Netw
; 118: 32-42, 2019 Oct.
Article
en En
| MEDLINE
| ID: mdl-31228722
ABSTRACT
Recently, the echo state networks (ESNs) have been widely used for time series prediction. To meet the demand of actual applications and avoid the overfitting issue, the online sequential ESN with sparse recursive least squares (OSESN-SRLS) algorithm is proposed. Firstly, the â0 and â1 norm sparsity penalty constraints of output weights are separately employed to control the network size. Secondly, the sparse recursive least squares (SRLS) algorithm and the subgradients technique are combined to estimate the output weight matrix. Thirdly, an adaptive selection mechanism for the â0 or â1 norm regularization parameter is designed. With the selected regularization parameter, it is proved that the developed SRLS shows comparable or better performance than the regular RLS. Furthermore, the convergence of OSESN-SRLS is theoretically analyzed to guarantee its effectiveness. Simulation results illustrate that the proposed OSESN-SRLS always outperforms other existing ESNs in terms of estimation accuracy and network compactness.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Redes Neurales de la Computación
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Neural Netw
Asunto de la revista:
NEUROLOGIA
Año:
2019
Tipo del documento:
Article