Creator:

Huk, Maciej

Contributor:

Korbicz, Józef (1951- ) - red. ; Uciński, Dariusz - red.

Title:

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

Group publication title:

AMCS, Volume 22 (2012)

Subject and Keywords:

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

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.

Publisher:

Zielona Góra: Uniwersytet Zielonogórski

Date:

2012

Resource Type:

artykuł

DOI:

10.2478/v10006-012-0034-5

Pages:

449-459

Source:

AMCS, Volume 22, Number 2 (2012) ; click here to follow the link

Language:

eng

License CC BY 4.0:

click here to follow the link

Rights:

Biblioteka Uniwersytetu Zielonogórskiego