The paper is devoted to the description of a new multi-purpose intellectual decision support system. We present the algorithms used and the results achieved in applying the system to analyzing and forecasting the sea ice area in the Northern Hemisphere. The impact of solar radiation on the changes in the sea ice area was confirmed. Application of interval neural nets to medium-term forecasting of sea ice area changes was justified.
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.