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On-line Inference of Finite Automata in Noisy Environments

  • Ivan Gabrijel
  • Andrej Dobnikar
Conference paper

Abstract

The most common type of noise in continuous systems of the real world is Gaussian noise, whereas discrete environments are usually subject to noise of a discrete type. The established, original solution for on-line inference of finite automata that is based on generalized recurrent neural networks is evaluated in the presence of noise of both types. It showed quite good performance and robustness.

Keywords

Problem Domain Recurrent Neural Network Noisy Environment Finite Automaton Continuous Type 
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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References

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    Gabrijel, I., Dobnikar, A. (2003) On-line identification and reconstruction of finite automata with generalized recurrent neural networks. Neural networks 16(1): 101–120.CrossRefGoogle Scholar
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Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • Ivan Gabrijel
    • 1
  • Andrej Dobnikar
    • 2
  1. 1.Private ResearcherTrebnjeSlovenia
  2. 2.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

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