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


Christopher Godwin Udomboso (PhD)
Olamide Olufunmilayo Ilori

Abstract

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.


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eISSN: 0855-0395