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Artificial Neural Nets and Genetic Algorithms pp 459–463Cite as

Modeling Complex Symbolic Sequences with Neural Based Systems

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Abstract

We study the problem of modeling long, complex symbolic sequences with recurrent neural networks (RNNs) and stochastic machines (SMs). RNNs are trained to predict the next symbol and the training process is monitored with information theory based performance measures. SMs are constructed using Kohonen self-organizing map quantizing RNN state space. We compare generative models through entropy spectra computed from sequences, or directly from the machines.

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References

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Slovak University of Technology, Ilkovicova 3, 812 19, Bratislava, Slovakia

    P. Tiňo & V. Vojtek

Authors
  1. P. Tiňo
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  2. V. Vojtek
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© 1998 Springer-Verlag Wien

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Tiňo, P., Vojtek, V. (1998). Modeling Complex Symbolic Sequences with Neural Based Systems. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_101

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  • DOI: https://doi.org/10.1007/978-3-7091-6492-1_101

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-83087-1

  • Online ISBN: 978-3-7091-6492-1

  • eBook Packages: Springer Book Archive

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