The dynamic universality of sigmoidal neural networksWe investigate the computational power of recurrent neural networks that apply the sigmoid activation function _(x)=[2 (1+e &x)]&1....
An application of multilayer neural network on hepatitis disease diagnosis using approximations of sigmoid activation functionObjective: Implementation of multilayer neural network (MLNN) with sigmoid activation function for the diagnosis of hepatitis disease.Methods:...
Vlsi implementation of a neural network classifier based on the saturating linear activation function... The hardware implementation of such a network presents a significant advantage in terms of circuit complexity as compared to a network based on a sigmoid activation function, but without compromising the classification performance....
A model of invariant object recognition in the visual system: learning rules, activation functions, lateral inhibition, and information-based performance measuresthis article, we introduce for VisNet2 a sigmoid activation function....
