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On-Line Identification and Rule Extraction of Finite State Automata with Recurrent Neural Networks

  • Ivan Gabrijel
  • Andrej Dobnikar

Abstract

The on-line identification of an unknown finite state automaton with a generalized recurrent neural network and an on-line learning scheme, together with an on-line rule extraction algorithm is presented. Several tests were made on different, strongly connected automata with structures ranging between 2 and 32 states and the results of both training and extraction processes are very promising.

Keywords

Recurrent Neural Network Generalize Architecture Rule Extraction Finite State Automaton Deterministic Finite State Automaton 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • Ivan Gabrijel
  • Andrej Dobnikar
    • 1
  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

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