Simulation of statistical distributions in the space of parameters of the solar wind and interplanetary magnetic field using artificial neural networks

Extended statistical and cross-correlational analyses of the data time seri es of the parameters of the solar activity (the Wolf numbers, the radio-emi ssion flux F10.7, and the global magnetic field of the Sun), the solar wind (velocity, density, and temperature), and the interplanetary magnetic fiel d are performed. All the parameters involved have distributions that, as a whole, are close to the lognormal distributions. Integro-cross-correlationa l analysis with a floating averaging period showed that the maximum pair co rrelation is observed For all parameters for the averaging period of the or der of one year. The attempt of complex simulation and forecast of annual s moothed values of the parameters with the use of artificial neural networks leads us to conclude that a rapid degeneration of the forecast occurs at a time scale of the order of one year. This is due to the high degree of cha otization of the distributions of statistical parameters, which are charact erized by rather high informational entropy, lying usually in the range 0.8 -0.9.