title: On-line identification and reconstruction of finite automata with generalized recurrent neural networks.
abstract: In this paper finite automata are treated as general discrete dynamical systems from the viewpoint of systems theory. The unconditional on-line identification of an unknown finite automaton is the problem considered. A generalized architecture of recurrent neural networks with a corresponding on-line learning scheme is proposed as a solution to the problem. An on-line rule-extraction algorithm is further introduced. The architecture presented, the on-line learning scheme and the on-line rule-extraction method are tested on different, strongly connected automata, ranging from a very simple example with two states only to a more interesting and complex one with 64 states; the results of both training and extraction processes are very promising.
authors.affiliation: Faculty of Computer and Information Science, University of Ljubljana, Trzaska c. 25, SI-1001, Ljubljana, Slovenia. ivan.gabrijel@fri.uni-lj.si
authors.forename: Ivan
authors.initials: I
authors.lastname: Gabrijel
authors.forename: Andrej
authors.initials: A
authors.lastname: Dobnikar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D009415:Nerve Net