(6th-February-2021)
• The auto-associative neural network is a special kind of MLP - in fact, it normally consists of two MLP networks connected "back to back“. The other distinguishing feature of auto-associative networks is that they are trained with a target data set that is identical to the input data set.
In training, the network weights are adjusted until the outputs match the inputs, and the values assigned to the weights reflect the relationships between the various input data elements. This property is useful in, for example, data validation: when invalid data is presented to the trained neural network, the learned relationships no longer hold and it is unable to reproduce the correct output. Ideally, the match between the actual and correct outputs would reflect the closeness of the invalid data to valid values. Auto-associative neural networks are also used in data compression applications.
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