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|Table of Contents Multivariate Data Modeling Neural Networks Models of Neural Networks|
|See also: ANN introduction|
Artificial neural networks (ANN) are adaptive models that can establish almost any relationship between data. They can be regarded as black boxes to build mappings between a set of input and output vectors. ANNs are quite promising in solving problems where traditional models fail, especially for modeling complex phenomena which show a non-linear relationship.
Neural networks can be roughly divided into three categories:
What these types of networks have in common is that they "learn" by adapting their network parameters. In general, the learning algorithms try to minimize the error of the model. This is often a type of gradient descent approach - with all its pitfalls.
Last Update: 2006-Jšn-17