Multidimensional mutual ordering of patterns using a set of pre-trained artificial neural networks
The article shows that large artificial neural networks can be used for mutual ordering of a set of multi-dimensional patterns of the same nature (handwritten text, voice, smells, taste). Each neural network must be pre-trained to recognize one of the patterns. As a measure of ordering one can use the entropy of patterns "Strangers" that are input to a neural network trained to recognize only examples of the pattern "familiar". The neural network after training reduces the entropy of the examples of the pattern "Familiar" and increases the entropy of examples of pattern "Stranger." It is shown that the entropy measure of the ordering always has two global minima. The first minimum corresponds to the pattern "Familiar", the second to the inversion of the pattern "Familiar". It is also shown that the Hamming distance between the patterns belonging to two different groups (groups of the two global minima) is always as large as possible.