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Investigation of effect of number of hidden neurons in Statistical Neural Network


T. O. James; S. U. Gulumbe; A. Danbab

JOURNAL: Journal of the Nigerian Statistical Association Vol. 29, 2017
YEAR: 2017


One of the most important characteristics of a perception network is the number of neurons in the hidden layer(s) and researchers have had a problem in determining the number of hidden units to obtain optimal network performance. This study employed Statistical Neural Network (SNN) using hidden neuron in predicting mother to child transmission of HIV. The data obtained from ANC of a certain hospital and simulated data were used for the study. Mean Square Error (MSE), Akaike Information Criteria (AIC) and Neural Information Criteria (NIC) were computed for determining the adequacy of the hidden neuron that determines the optimality of the model. The best result shows that the hidden neuron that determines the optimality of the model in the prediction of HIV with CD4 as an input variable with 7 neurons is SNN (1-2-1).


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