Korbicz, Józef - red. ; Uciński, Dariusz - red.
Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network
Group publication title:
Subject and Keywords:
artificial neural networks ; selective attention ; self consistency ; error backpropagation ; delta rule
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.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, Volume 22, Number 2 (2012) ; click here to follow the link