On the Level of Precision of a Heterogeneous Transfer Function in a Statistical Neural Network Model

A heterogeneous function of the statistical neural network is presented from two transfer functions: symmetric saturated linear and hyperbolic tangent sigmoid. The precision of the derived heterogeneous model over their respective homogeneous forms are established, both at increased sample sizes hidden neurons. Results further show the sensitivity of the heterogeneous model to increase in hidden neurons

On Some Properties of a Heterogeneous Transfer Function Involving Symmetric Saturated Linear (SATLINS) with Hyperbolic Tangent (TANH) Transfer Functions

For transfer functions to map the input layer of the statistical neural network model to the output layer perfectly, they must lie within bounds that characterize probability distributions. The heterogeneous transfer function, SATLINS_TANH, is established as a Probability Distribution Function (p.d.f), and its mean and variance are shown

A DERIVED HETEROGENEOUS TRANSFER FUNCTION FROM CONVOLUTION OF SYMMETRIC HARDLIMIT AND HYPERBOLIC TANGENT SIGMOID TRANSFER FUNCTIONS

This study derived a new heterogeneous transfer function of the Statistical Neural Network from a convolution of two transfer functions: the Symmetric Hard Limit and Hyperbolic Tangent Sigmoid, showing their various mathematical forms. The properties of the derived function were examined. Results show that it is a proper probability distribution with distributional properties shown to exist with mean 0, and variance . Numerical illustrations showed that the derived heterogeneous model is more efficient than its homogeneous forms, as indicated from their respective predictive performances. From the foregoing, the use of homogeneous models of the statistical neural networks in solving empirical problems is encouraged, for effective outcomes

An Adjusted Network Information Criterion for Model Selection in Statistical Neural Network Models

In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC) criterion, based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The ANIC improves model selection in more sample sizes than does the NIC

Bio-social correlates of intention to use or not to use contraception: The case of Ghana and Nigeria

Based on the 2008 and 2013 Demographic and Health Survey data of Ghana and Nigeria respectively, statistical neural network and logit regression models were used to examine the effects of selected bio-social factors on the intention to use contraception among never married and ever married women in the two countries. Results showed that on the whole, the SNN model identified more biosocial factors affecting the intention to use contraception in the two countries than did the logit model. Educational attainment, exposure to media, and visitation to a health facility affected intention to use contraception significantly and positively in both countries. On the other hand, number of living children, infrequent sexual intercourse, postpartum amenorrhea, opposition to contraception and lack of access to contraceptives negatively affected intention to use contraception. The study findings have underscored the rational nature of the decisions women make in using contraception or not.