Neural networks training using the constructivism paradigms


Autoria(s): Teixeira, Marcelo Carvalho Minhoto; Lamas, Decio
Contribuinte(s)

Universidade Estadual Paulista (UNESP)

Data(s)

27/05/2014

27/05/2014

01/12/1995

Resumo

The Backpropagation Algorithm (BA) is the standard method for training multilayer Artificial Neural Networks (ANN), although it converges very slowly and can stop in a local minimum. We present a new method for neural network training using the BA inspired on constructivism, an alphabetization method proposed by Emilia Ferreiro based on Piaget philosophy. Simulation results show that the proposed configuration usually obtains a lower final mean square error, when compared with the standard BA and with the BA with momentum factor.

Formato

546-549

Identificador

http://dx.doi.org/10.1109/MWSCAS.1995.504497

Midwest Symposium on Circuits and Systems, v. 1, p. 546-549.

http://hdl.handle.net/11449/64676

10.1109/MWSCAS.1995.504497

WOS:A1996BF75Z00135

2-s2.0-0029463724

Idioma(s)

eng

Relação

Midwest Symposium on Circuits and Systems

Direitos

closedAccess

Palavras-Chave #Adaptive filtering #Backpropagation #Computer simulation #Errors #Learning algorithms #Learning systems #Low pass filters #Alphabetization method #Backpropagation algorithm #Constructivism paradigms #Mean square error #Momentum factor #Neural networks training #Piaget philosophy #Neural networks
Tipo

info:eu-repo/semantics/conferencePaper