All
neurons in a neural network take numeric input and produce numeric output.
The activation function of a neural unit can accept input values in any
range and produces output in a strictly limited range. Although the input
can be in any range, there is a saturation effect so that the unit is
only sensitive to inputs within a fairly limited range. For example consider
the logistic function. In this case, the output is in the range *(0,1)*, and the input is sensitive in a range not
much larger than *(-1,+1)*. Thus, for a wide range of input values
ranging outside *(-1, +1)*, the output of a logistic neuron is approximately
the same. This saturation effect will severely limit the ability of a
network from capturing the underlying input-target relationship.

The
above problem can be solved by limiting the numerical range of the original
input and target variables. This process is known as scaling, which is
one of the most commonly used forms of preprocessing. *STATISTICA
Automated Neural Networks* scales the input and target variables using
linear transformations such that the original minimum and maximum of every
variable is mapped to the range *(0, 1)*.

There are other important reasons for standardization of the variables. One is related to weight decay. Standardizing the inputs and targets will usually make the weight decay regularization more effective. Other reasons include the original variable scaling and units of measure. It is often the case that variables in the original data set have substantially different ranges (i.e., different variances). This may have to do with the units of measurements or simply the nature of the variables themselves. However, the numeric range of a variable, among many, may not be a good indication of how important that variable is.