Herself’s Artificial Intelligence

Associative Memories

Associate memory stores information by associating or correlating it with other memories. Most neural nets have the capability to store memory this way. Associate memory systems can recall information based on garbled input, details are stored in a distributive fashion, are accessible by content, are very robust, and most importantly can generalize. The two classes of associative memory classified by how they store memories are: auto associative; hetero-associative.

Autoassociate: each data item is associated with itself. Used for cleaning up and recognizing handwriting. Training is done by giving the same pattern to the input and output nodes.

Hetero-associative: different data items are associated with each other. One pattern is given and another is output, a translation program would fall in this category. This one is trained by giving one input pattern to the input nodes and the desired output pattern to the output nodes.

The main architectures for associated memory neural networks are: crossbar (aka Hopfield); adaptive filter networks; competitive filter networks. Adaptive filter networks, like Adelines, test each neurode to see if it is the pattern specific to that neurode. These are used in signal processing.

Competitive filter networks, like Kohonens, have neurodes competing to be the one that matches the pattern. They self-organize and they perform statistical modeling with out outside aid or input.