  In this paper we present a modified version of the Hyperbolic Tangent
Activation Function as a learning unit generator for neural networks. The
function uses an integer calibration constant as an approximation to the Euler
number, e, based on a quadratic Real Number Formula (RNF) algorithm and an
adaptive normalization constraint on the input activations to avoid the
vanishing gradient. We demonstrate the effectiveness of the proposed
modification using a hypothetical and real world dataset and show that lower
run-times can be achieved by learning algorithms using this function leading to
improved speed-ups and learning accuracies during training.

Neural and Evolutionary Computing