. Interpretation of models induced by artificial neural networks is often a difficult task. In this paper we focus on a relatively novel neural network architecture and learning algorithm, bp-som, that offers possibilities to overcome this difficulty. It is shown that networks trained with bp-som show interesting regularities, in that hidden-unit activations become restricted to discrete values, and that the som part can be exploited for automatic rule extraction. 1 Introduction Nowadays artificial neural networks (anns) are successfully used in industry and commerce. However, the interpretation of anns is still an obstacle: "For anns to gain a even wider degree of user acceptance and to enhance their overall utility as learning and generalization tools, it is highly desirable if not essential that an explanation capability becomes an integral part of the functionality of a trained ann."[ADT1995]. bp-som is an relatively novel neural network architecture and learning algorithm which..
To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.