Neural networks training using the constructivism paradigms
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Data
1995-12-01
Autores
Teixeira, Marcelo Carvalho Minhoto [UNESP]
Lamas, Decio [UNESP]
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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.
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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
Como citar
Midwest Symposium on Circuits and Systems, v. 1, p. 546-549.