A new approach for extracting rules from a trained neural network
Artificial Neural Networks perform adaptive learning. This advantage can be used to complete and improve the knowledge acquisition in knowledge engineering by rule extraction from a trained neural network. This paper proposes a new rule extraction method based on MACIE algorithm, which has been improved so that it can be used in neural networks with continuous inputs and outputs, obtaining a global and continuous set of production rules in a very efficient way. An application example to obtain the average load demand of a power plant is also shown.
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