Extraction of crisp logical rules using constrained backpropagation networks 

W\lodzis\law Duch, Rafa\l Adamczak and Krzysztof Grabczewski, 
Department of Computer Methods, Nicholas Copernicus University, Grudziadzka 5,
87-100 Torun, Poland.  

Masumi Ishikawa and Hiroki Ueda
Department of Control Engineering and Science, Kyushu Institute of Technology,
680-4 Kawazu, Iizuka, Fukuoka 820, Japan


Two recently developed methods for extraction of crisp logical rules from
neural networks trained with backpropagation algorithm are compared. Both
methods impose constraints on the structure of the network by adding
regularization terms to the error function. Networks with minimal number of
connections are created, leading to a small number of crisp logical rules. The
two methods are compared on the Iris and mushroom classification problems,
generating the simplest logical description of this data published so far. 


Proc. of the European Symposium on Artificial Neural Networks (ESANN'97),
Brugge, Belgium 16-18.4.1997, pp. xx-xx