Abstract
The paper presents a parallel algorithm for the parametric identification of dynamical systems with interval parameters. The algorithm is based on the previously developed, substantiated and tested adaptive interpolation algorithm, which makes it possible to explicitly obtain the dependence of the states of a dynamic system on interval parameters. The solution of the problem of parametric identification is reduced to the problem of minimizing a certain objective function in the space of boundaries of interval parameter estimates. Due to the use of the adaptive interpolation algorithm when calculating the gradient of the objective function, there is no need for additional analysis and modeling of the original dynamic system, so it is convenient to use first-order methods for optimization. However, the task of calculating the objective function and the gradient includes a set of conditional minimization problems for explicit functions that can be solved independently of each other. The article discusses the main aspects and features of parallelization and implementation of the parametric identification algorithm and tests it on several representative examples. The acceleration and efficiency of parallelization are analyzed.
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