Evolutionary training for Dynamical Recurrent Neural Networks: an application in finantial time series prediction.

Delgado, Miguel, Pegalajar, M. Carmen, and Pegalajar Cuéllar, Manuel. "Evolutionary training for Dynamical Recurrent Neural Networks: an application in finantial time series prediction.." Mathware and Soft Computing 13.2 (2006): 89-110. <http://eudml.org/doc/41874>.

@article{Delgado2006,
abstract = {Theoretical and experimental studies have shown that traditional training algorithms for Dynamical Recurrent Neural Networks may suffer of local optima solutions, due to the error propagation across the recurrence. In the last years, many researchers have put forward different approaches to solve this problem, most of them being based on heuristic procedures. In this paper, the training capabilities of evolutionary techniques are studied, for Dynamical Recurrent Neural Networks. The performance of the models considered is compared in the experimental section, in real finantial time series prediction problems.},
author = {Delgado, Miguel, Pegalajar, M. Carmen, Pegalajar Cuéllar, Manuel},
journal = {Mathware and Soft Computing},
keywords = {Redes neuronales; Algoritmos evolutivos; Series temporales; evolutionary algorithms; recurrent neural networks},
language = {eng},
number = {2},
pages = {89-110},
title = {Evolutionary training for Dynamical Recurrent Neural Networks: an application in finantial time series prediction.},
url = {http://eudml.org/doc/41874},
volume = {13},
year = {2006},
}

TY - JOUR
AU - Delgado, Miguel
AU - Pegalajar, M. Carmen
AU - Pegalajar Cuéllar, Manuel
TI - Evolutionary training for Dynamical Recurrent Neural Networks: an application in finantial time series prediction.
JO - Mathware and Soft Computing
PY - 2006
VL - 13
IS - 2
SP - 89
EP - 110
AB - Theoretical and experimental studies have shown that traditional training algorithms for Dynamical Recurrent Neural Networks may suffer of local optima solutions, due to the error propagation across the recurrence. In the last years, many researchers have put forward different approaches to solve this problem, most of them being based on heuristic procedures. In this paper, the training capabilities of evolutionary techniques are studied, for Dynamical Recurrent Neural Networks. The performance of the models considered is compared in the experimental section, in real finantial time series prediction problems.
LA - eng
KW - Redes neuronales; Algoritmos evolutivos; Series temporales; evolutionary algorithms; recurrent neural networks
UR - http://eudml.org/doc/41874
ER -