Аннотация:The paper presents a short review of various directions
of Artificial Neural Network (ANN) applications to
modelling of near Earth space radiation distribution and
dynamics and to the development of some methods of
space weather forecasting. ANN models may be based
on well-known physical laws or on some empirical
rules. We can present four main directions of ANN
applications. The first one is the development of strong
non-linear quasi-stationary models with large number of
input nodes. The examples of results in the first
direction are: 3D model of the Earth's magnetopause
and mapping of the near Earth high energy particle
(electrons and protons) distribution. The second
direction is modelling of cumulative and time shifted
effects in the time series. The main problem in these
models is searching the most significant measured
physical parameters as input nodes and determination of
the most appropriate time intervals for averaging or
shifting parameter values. This direction permits to
develop dynamical models of physical processes in
multi-parametric time series. One of the models
developed in the second direction is the model of the
slot region of Earth's radiation electron belt dynamics
depending on the solar wind conditions. The third
direction is modelling of self-consistent time series by
means of recurrent ANNs. These models take into
account the information about prehistory of the system
dynamics and hence they may be used for forecasting.
The models forecasting sunspot number and average
solar wind conditions are excellent examples of
investigations in the third direction. The fourth direction
is combination of the described directions by means of
global ANN on the base of some classification rules
which may be used in future for the development of an
expert system for the space weather forecasting.