An improved backpropagation algorithm to avoid the local minima problem

We propose an improved backpropagation algorithm intended to avoid the local minima problem caused by neuron saturation in the hidden layer. Each training pattern has its own activation functions of neurons in the hidden layer. When the network outputs have not got their desired signals, the activat...

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Bibliographic Details
Published inNeurocomputing (Amsterdam) Vol. 56; pp. 455 - 460
Main Authors Wang, X.G., Tang, Z., Tamura, H., Ishii, M., Sun, W.D.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 2004
Subjects
Backpropagation
Gain parameter
Local minima
Saturation
Backpropagation
Local minima
Saturation
Gain parameter
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Summary:We propose an improved backpropagation algorithm intended to avoid the local minima problem caused by neuron saturation in the hidden layer. Each training pattern has its own activation functions of neurons in the hidden layer. When the network outputs have not got their desired signals, the activation functions are adapted so as to prevent neurons in the hidden layer from saturating. Simulations on some benchmark problems have been performed to demonstrate the validity of the proposed method.
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ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2003.08.006