Purpose. Increasing accuracy of dynamic models of the complex nonlinear processes for solution of the tasks for these processes control. Methodology. Structural-parametric identification of nonlinear dynamic processes including the identification of the model structure based on selection by non-shift criterion, as well as self-reactance identification of the optimum structure model by the regularity criterion through the whole sampling of experimental data. Findings. The algorithms of global and local optimization of nonlinear dynamic process models realizing the procedure of structural-parametric identification by their structural and self-reactance optimization were developed, which allows getting the models of extra accuracy. Originality. The method of identification of nonlinear dynamic processes consisting of procedures for estimation of the state and characteristics of the process, as well as their structural-parametric identification, was offered. It allows, unlike the known of methods, to fulfil the identification of these processes in the batch mode by structural-parametric and in real-time mode by self-reactance optimization of their models. Practical value. The results of the research can be used while developing algorithms for controlling complex nonlinear processes based on their complex estimation and identification.

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