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Erschienen in:
1998 | OriginalPaper | Buchkapitel
Modeling Complex Symbolic Sequences with Neural Based Systems
verfasst von : P. Tiňo, V. Vojtek
Erschienen in: Artificial Neural Nets and Genetic Algorithms
Verlag: Springer Vienna
Enthalten in: Professional Book Archive
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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.