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Book

2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)

M.: IEEE, 2016.

We demonstrate that classical quadratic forms are not able to solve the problem of recognizing high­dimensional images. The "deep" Galushkin­Hinton neural networks  can solve the problem of high­dimensional image recognition, but their training has exponential computational complexity. It is technically impossible to train and retrain a "deep" neural network rapidly. For mobile "artificial nose" systems we proposed to employ a number of "wide" neural networks trained in accordance with (GOST R 52633.5­2011).  This standardized learning algorithm has a linear computational complexity, i.e. for each new smell image a time of about 0.3 seconds is sufficient for creating and training a new neural network with 2024 inputs and 256 outputs. This leads to the possibility of the rapid training of the artificial intelligence "artificial nose" and a gradual expansion of its database consisting of 10 000 or more trained artificial neural networks.

Chapters
2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)