Neural Learning of Approximate Simple Regular Languages

Discrete-time recurrent neural networks (DTRNN) have been used to infer DFA from sets of examples and counterexamples; however, discrete algorithmic methods are much better at this task and clearly outperform DTRNN in space and time complexity. We show, however, how DTRNN may be used to learn not the exact language that explains the whole learning set but an approximate and much simpler language that explains a great majority of the examples by using sim- ple rules. This is accomplished by gradually varying the error function in such a way that the net is eventually allowed to classify clearly but incorrectly those strings that are diÆcult to learn, which are treated as exceptions. The results show that in this way, the DTRNN usually learns a simplified approximate language.