Abstract
In this paper we consider in the developing conception of multi-valued neurons. First of all significant reinforcement of the learning algorithm which led to the 20–30 — times acceleration of the convergence of learning is proposed. Then neural network based on multi-valued neurons where each neuron is connected with restricted number of other ones (function of connections is defined as random function) is considered. Application of such an network to image recognition is proposed. Then approach to extrapolation of the temporal series based on the representation of the series as multiple-valued function, learning of the single neural element and furtheron forecasting of the function's values is also considered.
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References
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© 1995 Springer-Verlag Berlin Heidelberg
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Aizenberg, N.N., Aizenberg, I.N., Krivosheev, G.A. (1995). Multi-valued neurons: Learning, networks, application to image recognition and extrapolation of temporal series. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_200
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DOI: https://doi.org/10.1007/3-540-59497-3_200
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