Pseudo-distance based artificial neural network training

The goal of this work is to create a model based on an artificial neural network for prediction in a real world domain. Information on prediction errors price function (reflecting the monetary value of a loss) is acquired from a power and heat production expert. Systematic errors and not acceptable types of errors in specific situations are identified and a solution using superposition of the price function on Euclidian distance is proposed. This approach can be considered as a special case of pseudo distance theory implementation. There is a functional prediction system in the observed power and heat producing company which makes the information retrieval in cooperation with the domain expert less difficult. The knowledge on the price and on the not acceptable errors is used for modification of the learning error function. The experimental results of the proposed algorithm are obtained on the heat production data. Our model has approximately the same percentage accuracy as the existing model but using the expensive errors suppression, predictions of the new model lead to production costs reduction.