Isarithmic flow control using learning automata
Athanasios V. Vasilakos, A. Haritsis, S. Batistatos (1990)
Kybernetika
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Athanasios V. Vasilakos, A. Haritsis, S. Batistatos (1990)
Kybernetika
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Athanasios V. Vasilakos (1989)
Kybernetika
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Ioannis Gragopoulos, Fotini - Niovi Pavlidou (1994)
The Yugoslav Journal of Operations Research
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Andrzej Kasiński, Filip Ponulak (2006)
International Journal of Applied Mathematics and Computer Science
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In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods?...
Lim, J.-T., Meerkov, S.M. (1996)
Mathematical Problems in Engineering
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Roman Zajdel (2013)
International Journal of Applied Mathematics and Computer Science
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In this article, a new class of the epoch-incremental reinforcement learning algorithm is proposed. In the incremental mode, the fundamental TD(0) or TD(λ) algorithm is performed and an environment model is created. In the epoch mode, on the basis of the environment model, the distances of past-active states to the terminal state are computed. These distances and the reinforcement terminal state signal are used to improve the agent policy.