Nonparametric algorithms for restoration of random fields

Numerous practical tasks are closely connected with the need to restore fields of one nature or another from noisy experimental data. A feature of this problem is that a priori information isn't often enough for the description of this field accurately to within a set of a vector of parameters. This is due to the fact that information can be polytypic on the various channels of multidimensional processes. It means that the information matches to various levels of a priori in- formation. In this article special attention is paid to this problem. The corresponding algorithms of identification are used in the presence of a priori information of parametric type. In the presence of a priori information of parametric type, it is advisable to use the appropriate identification algorithms when the structure of field models is defined accu- rately to within a set of parameters and their subsequent evaluation, as current information arrives. If a priori informa- tion isn't enough, it is expedient to the researcher to use nonparametric estimates of Nadaraya-Watson for restoration of the respective fields. At the same time it is essential to determine whether all the channels of the multidimensional system are N- or T-processes. N- or T-processes are processes at which entrance or output components are stochastic - dependent and this dependence is unknown in most cases. The fields of distribution of impurity of harmful substances in atmospheric air of the city are considered as an ex- ample of use of similar processes. Nonparametric assessment of function of regression is applied as an algorithm of restoration of this field.