Authors:
M. P. Cuéllar
;
M. Delgado
and
M. C. Pegalajar
Affiliation:
E.T.S. Ingeniería Informática. Univerity of Granada, Spain
Keyword(s):
Non-Linear Programming, Recurrent Neural Networks, Time Series Prediction
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Artificial Neural Networks are bioinspired mathematical models that have been widely used to solve many complex problems. However, the training of a Neural Network is a difficult task since the traditional training algorithms may get trapped into local solutions easily. This problem is greater in Recurrent Neural Networks, where the traditional training algorithms sometimes provide unsuitable solutions. Some evolutionary techniques have also been used to improve the training stage, and to overcome such local solutions, but they have the disadvantage that the time taken to train the network is high. The objective of this work is to show that the use of some non-linear programming techniques is a good choice to train a Neural Network, since they may provide suitable solutions quickly. In the experimental section, we apply the models proposed to train an Elman Recurrent Neural Network in real-life Time Series Prediction problems.