Heuristics for the extraction of rules from discrete-time recurrent neural networks

It is pointed out that discrete recurrent neural networks can learn to classify long strings of a regular language correctly when trained on a small finite set of positive and negative example strings. Rules defining the learned grammar can be extracted from networks by applying clustering heuristics in the output space of recurrent state neurons. Empirical evidence that there exists a correlation between the generalization performance of recurrent neural networks for regular language recognition and the rules that can be extracted from a neural network is presented. A heuristic that makes it possible to extract good rules from trained networks is given, and the method is tested on networks that are trained to recognize a simple regular language. >