A possible method for estimating the unknown parameters of dynamic models described by differential-algebraic equations is considered. The parameters are estimated using the observations of a mathematical model. The parameter values are found by minimizing a criterion written as the sum of the squared deviations of the values of the state vector’s coordinates from their exact counterparts obtained through measurements at different time instants. Parallelepiped-type constraints are imposed on the parameter values. For solving the optimization problem, a mini-batch method of adaptive random search is proposed, which further develops the ideas of optimization methods used in machine learning. This method is applied for solving three model problems, and the results are compared with those obtained by gradient optimization methods of machine learning procedures and also with those obtained by metaheuristic algorithms.

考虑一种可能的方法来估计由微分代数方程描述的动态模型的未知参数。使用数学模型的观察值估计参数。通过最小化一个标准来找到参数值，该标准被写为状态矢量坐标值与通过在不同时刻的测量获得的精确对应值的平方偏差的平方偏差之和。平行六面体类型约束施加在参数值上。为了解决优化问题，提出了一种自适应随机搜索的小批量方法，进一步发展了用于机器学习的优化方法的思想。该方法用于解决三个模型问题，