# Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network

International Journal of Applied Mathematics and Computer Science (2012)

- Volume: 22, Issue: 2, page 449-459
- ISSN: 1641-876X

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topMaciej Huk. "Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network." International Journal of Applied Mathematics and Computer Science 22.2 (2012): 449-459. <http://eudml.org/doc/208121>.

@article{MaciejHuk2012,

abstract = {In this paper the Sigma-if artificial neural network model is considered, which is a generalization of an MLP network with sigmoidal neurons. It was found to be a potentially universal tool for automatic creation of distributed classification and selective attention systems. To overcome the high nonlinearity of the aggregation function of Sigma-if neurons, the training process of the Sigma-if network combines an error backpropagation algorithm with the self-consistency paradigm widely used in physics. But for the same reason, the classical backpropagation delta rule for the MLP network cannot be used. The general equation for the backpropagation generalized delta rule for the Sigma-if neural network is derived and a selection of experimental results that confirm its usefulness are presented.},

author = {Maciej Huk},

journal = {International Journal of Applied Mathematics and Computer Science},

keywords = {artificial neural networks; selective attention; self consistency; error backpropagation; delta rule},

language = {eng},

number = {2},

pages = {449-459},

title = {Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network},

url = {http://eudml.org/doc/208121},

volume = {22},

year = {2012},

}

TY - JOUR

AU - Maciej Huk

TI - Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network

JO - International Journal of Applied Mathematics and Computer Science

PY - 2012

VL - 22

IS - 2

SP - 449

EP - 459

AB - In this paper the Sigma-if artificial neural network model is considered, which is a generalization of an MLP network with sigmoidal neurons. It was found to be a potentially universal tool for automatic creation of distributed classification and selective attention systems. To overcome the high nonlinearity of the aggregation function of Sigma-if neurons, the training process of the Sigma-if network combines an error backpropagation algorithm with the self-consistency paradigm widely used in physics. But for the same reason, the classical backpropagation delta rule for the MLP network cannot be used. The general equation for the backpropagation generalized delta rule for the Sigma-if neural network is derived and a selection of experimental results that confirm its usefulness are presented.

LA - eng

KW - artificial neural networks; selective attention; self consistency; error backpropagation; delta rule

UR - http://eudml.org/doc/208121

ER -

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