Autor:

Huk, Maciej

Współtwórca:

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

Tytuł:

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

Tytuł publikacji grupowej:

AMCS, Volume 22 (2012)

Temat i słowa kluczowe:

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.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2012

Typ zasobu:

artykuł

DOI:

10.2478/v10006-012-0034-5

Strony:

449-459

Źródło:

AMCS, Volume 22, Number 2 (2012) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego