The Application of Neural Networks in Prediction Problems. Rohitash Chandra, Godfrey Owubolu. IWIM, Prague, 2007.
Article (in pdf)
Abstract. This work presents the application of neural networks to real-life prediction problems. Neural networks are trained to predict three real world application problems given data for training and testing. We pre-process the actual output of the dataset by converting the values to integers and later translate them to binary strings. The sigmoidal output neurons of the feed-forward architecture predicts the output as binary values which are then translated back to integers to further compare the predicted output values of the network with the desired or actual output values from the dataset. The results for two, out of the three problems solved show that neural networks can be used to predict real-life problems similar to other inductive modeling approaches. A neural network can therefore be classified as an inductive modeling approach since it can be used for prediction, given the desired output . Finally, the performance of neural networks are then compared to GMDH.
Keywords. Prediction, artificial neural networks, inductive modelling
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