Abstract
A neural network that uses a pretuning procedure for function approximation is presented. Unlike traditional neural network algorithms in which changeable parameters are multiplicative weights of connections between neurons in the network, the pretuning procedure deals with additive thresholds of interneurons of the proposed neural network and is a dynamical combinatory inhibition of these neurons. It is shown that in this case the neural network can combine local approximation and distributed activation. The usefulness of the neural network with pretuning (NNP) for the tasks of search and reproduction of sensorimotor mapping of robot is briefly discussed.
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© 2006 Springer-Verlag Berlin Heidelberg
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Shepelev, I. (2006). A Neural Network Module with Pretuning for Search and Reproduction of Input-Output Mapping. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_96
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DOI: https://doi.org/10.1007/11840817_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-38625-4
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