An adaptive learning rule with limited error signals for training of multilayer perceptrons

Although an n-th order cross-entropy (nCE) error function resolves the incorrect saturation problem of conventional error backpropagation (EBP) algorithm, performance of multilayer perceptrons (MLPs) trained using the nCE function depends heavily on the order of nCE. In this paper, we propose an adaptive learning rate to markedly reduce the sensitivity of MLP performance to the order of nCE, Additionally, we propose to limit error signal values at output nodes for stable learning with the adaptive learning rate, Through simulations of handwritten digit recognition and isolated-word recognition tasks, it was verified that the proposed method successfully reduced the performance dependency of MLPs on the nCE order while maintaining advantages of the nCE function.
Publisher
Electronics Telecommunications Research Inst
Issue Date
2000-01
Language
ENG
Keywords

BACKPROPAGATION ALGORITHM; NEURAL NETWORKS; RECOGNITION; CONVERGENCE

Citation

ETRI JOURNAL, v.22, no.3, pp.10 - 18

ISSN
1225-6463
URI
http://hdl.handle.net/10203/69834
Appears in Collection
EE-Journal Papers(저널논문)
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