ON A COMPUTER RESEARCH OF K-MODELS
T. V. Maltseva, A. V. Medvedev, M. F. Reshetnev · CyberLeninK (CyberLeninka) · 2013
The paper deals with a research of output prediction algorithm for a system of nonlinear stochastic static objects that can not be decomposed, in partial parametric and nonparametric uncertainty. The authors set the goal to perform modeling in conditions brought nearer to real technological industrial process conditions, due to the prior information obtained. In the approach the theoretical aspects of parametric and nonparametric identification of stochastic objects are used; term of compound vectors that allows to take account of intrastation complexity is introduced; problems of distinct discrecity of variables measurement and account of different levels of the prior information are considered. The authors present a new type of models which is based on the following triad: basic laws, parameterized and nonparame-terized relations. The work contains new algorithms, models and computational investigations. The results can be ap-plied when modeling of technological processes of production and processing, as well as in description of objects char-acterized with multi-linked relations and complexity resulting from the lack of information. Keywords: prior information, complex system, nonparametric estimate, multilinked system, K-models, prog-nostication.