Sigmoid Function. An S-shaped curve, with a near-linear central response and saturating limits. See also, logistic function and hyperbolic tangent function.
//the total at our sigmoid function to get the output.
for (int j=0; j{ ...
By using a feed-forward network, the available learning samples are approximated by a single, smooth function consisting of a summation of sigmoid functions.
While ANNs often contain only sigmoid functions (and sometimes Gaussian functions), CPPNs can include both types of functions and many others.
The solution came however, with the development of neuron models that applied a sigmoid function to the weighted sum (w1x1+w2x2+...wnxn+wb) to make the activation of the neuron non-linear, scaled and differentiable (continuous).
You can save on CPU time by approximating the sigmoid functions with a series of straight lines, a piecewise-linear function.
The output is a real value which lies between 0 and 1 based on the sigmoid function. The formula for the output is,
Output = 1 / (1+e-sum)
As the sum increases, the output approaches 1. As the sum decreases, the output approaches 0.
most importantly, in their activation function, which consists of transforming the total net input by a sigmoid function, rather than simply thresholding it; ...
Other functions with similar features can be used, most commonly tanh() which has an output range of [-1,1]. The sigmoid function has the additional benefit of having an extremely simple derivative function for backpropagating errors through a ...
an activation function at the output that saturates at the two target values: such a function will be close to the target value for any net input that is sufficiently large and has the correct sign. Specifically, we use the logistic sigmoid function ...
 See also: Neural network, Perceptron, Percept, Classification, Activation function
  
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