Mini-batch adaptive random search method for the parametric identification of dynamic systems

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.