Modeling and Multivariate Methods - SAS

Chapter 10 Creating Neural Networks 279
Overview of Neural Networks
Overview of Neural Networks
Think of a neural network as a function of a set of derived inputs, called hidden nodes. The hidden nodes
are nonlinear functions of the original inputs. You can specify up to two layers of hidden nodes, with each
layer containing as many hidden nodes as you want.
Figure 10.2 shows a two-layer neural network with three X variables and one Y variable. In this example, the
first layer has two nodes, and each node is a function of all three nodes in the second layer. The second layer
has three nodes, and all nodes are a function of the three X variables. The predicted Y variable is a function
of both nodes in the first layer.
Figure 10.2 Neural Network Diagram
The functions applied at the nodes of the hidden layers are called activation functions. The activation
function is a transformation of a linear combination of the X variables. For more details about the activation
functions, see “Hidden Layer Structure” on page 283.
The function applied at the response is a linear combination (for continuous responses), or a logistic
transformation (for nominal or ordinal responses).
The main advantage of a neural network model is that it can efficiently model different response surfaces.
Given enough hidden nodes and layers, any surface can be approximated to any accuracy. The main
disadvantage of a neural network model is that the results are not easily interpretable, since there are
intermediate layers rather than a direct path from the X variables to the Y variables, as in the case of regular
regression.
Launch the Neural Platform
To launch the Neural platform, select Analyze > Modeling > Neural.
Launching the Neural platform is a two-step process. First, enter your variables on the Neural launch
window. Second, specify your options in the Model Launch.