Abstract
Using short-time correlation function measurements of an observed process as input, we show that it is possible to train a network to learn the non-linear stochastic dynamics underlying the process. Alternatively this can be formulated as a neural network with non-linear stochastic synapses, which can, after training, be used to associate actions.
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References
H.Haken, Information and Self-Organization, Springer-Verlag, Berlin-Heidelberg-New York 1988
L.Borland, H.Haken, Unbiased Determination of Forces Causing observed Processes, Z. Phys. B — Condensed Matter 81 (1992) 95
L.Borland, H. Haken, Unbiased Estimate of Forces from Measured Correlation Functions, including the Case of Strong Multiplicative Noise, Ann. Physik 1 (1992) 452
L.Borland, H. Haken, Learning the Dynamics of Two Dimensional Stochastic Markov Processes, to be published in Open Systems and Information Dynamics
E.T. Jaynes, Phs. Rev 106 (1957) 4,620
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© 1993 Springer-Verlag Berlin Heidelberg
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Borland, L., Haken, H. (1993). Learning networks for process identification and associative action. In: Mira, J., Cabestany, J., Prieto, A. (eds) New Trends in Neural Computation. IWANN 1993. Lecture Notes in Computer Science, vol 686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56798-4_222
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DOI: https://doi.org/10.1007/3-540-56798-4_222
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