Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1184

 Title: Classification of Data to Extract Knowledge from Neural Networks Authors: Martinez, AnaCastellanos, AngelGonzalo, Rafael Keywords: Neural NetworkBackpropagationControl Feedback MethodsModels of Computation Issue Date: 2009 Publisher: Institute of Information Theories and Applications FOI ITHEA Abstract: A major drawback of artificial neural networks is their black-box character. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, we use a method that can be used for symbolic knowledge extraction from neural networks, once they have been trained with desired function. The basis of this method is the weights of the neural network trained. This method allows knowledge extraction from neural networks with continuous inputs and output as well as rule extraction. An example of the application is showed. This example is based on the extraction of average load demand of a power plant. URI: http://hdl.handle.net/10525/1184 ISSN: 1313-0455 Appears in Collections: Book 08 Classification Forecasting Data Mining

Files in This Item:

File Description SizeFormat