You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
| You are working with the text-only light edition of "H.Lohninger: Teach/Me Data Analysis, Springer-Verlag, Berlin-New York-Tokyo, 1999. ISBN 3-540-14743-8". Click here for further information.
|
Table of Contents  Multivariate Data Modeling Neural Networks Models of Neural Networks Multi-layer Perceptron |
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| See also: processing unit, back-propagation |   |
A multi-layer perceptron consists of units
and connections. Each unit has an activation, and each link between two
units has a weight. The units are organized in layers. Three different
types of units are distinguished:
When viewing the neural network as a black box, the hidden units are not visible from the outside. The input units receive the input data, and the output units provide the output.
The calculation of the final output values proceeds layer by layer.
First, the input signals are applied to the input layer, and each neuron
of the input layer calculates its output value. Next, these values are
propagated to the next layer; and so forth, until the output layer is reached.
You can experiment with a simple feed-forward network by starting the following 
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Last Update: 2005-Jul-16