An application of non-linear programming to train Recurrent Neural Networks in Time Series Prediction problems

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.