The article discusses the solution of the problem of developing converters based on specified amplitude-frequency characteristics. The main problem is to carry out a large number of measuring measures with changes in the parameters of the transducers to achieve the necessary amplitude-frequency characteristics, which leads to high time and resource costs for development. The analysis of the main parameters of the converters affecting the specified amplitude-frequency characteristics is carried out. The existing approaches, methods and algorithms for creating converters of the required characteristics are analyzed. The development of a module for predicting the parameters of electromechanical converters based on specified amplitude-frequency characteristics is described. The research objectives include the creation of structural-parametric and mathematical models for calculating the characteristics of converters at the design stage. An algorithm for training a model based on experimental data obtained during measurements is described. The use of machine learning methods to predict parameters minimizes the number of experiments performed and reduces the cost of developing converters. The proposed approach is based on the use of the relationship between the design parameters of the converters and their frequency characteristics. The gradient boosting algorithm is used to increase the accuracy of forecasting. The stages of data preparation for model training are presented. The learning process of the model is described. The results demonstrate a significant reduction in the modeling time of the converters: the use of the module makes it possible to speed up the process several times compared with the experimental approach. Predicting characteristics based on a model provides comparable accuracy with a larger amount of data. The findings of the study confirm the effectiveness of the proposed approach in the development of converters, reducing time and financial costs, increasing the accuracy of modeling and applicability in conditions of limited resources.
