This paper is devoted to the application of machine learning methods to the problem of identifying parameters of dynamic systems. The system under consideration is the Twin Rotor MIMO System mechatronic bench, which is a simplified model of a helicopter with two degrees of freedom. The dynamics of motion are described by a system of nonlinear differential equations, which does not allow the use of classical methods based on parameterization of the model in the form of linear regression. To solve the problem, the following methods were used: multilayer perceptron, polynomial regression, Bayesian regression and ensemble of models. Conducted exploratory data analysis to evaluate the quality of the dataset. A comparative analysis and evaluation of the obtained results are presented.