The article presents a simulation-dynamic model for calculating the financial leverage efficiency level. The model was developed on the basis of the system dynamics method using Powersim Studio tools. In the constructed model, the calculations were carried out on the example of PJSC «Koks», one of the largest Russian producers and exporters of metallurgical coke. The model was used as a tool to study the capital structure of the enterprise. As a result of the experiments, the level values of financial leverage efficiency, differential of financial leverage and financial leverage ratio were calculated. On the basis of the calculations, the conclusions were made about the quality of management of borrowed capital in the enterprise. In addition the assessment of the developed model of financial leverage efficiency was carried out; as a result of the assessment, both the model advantages and its disadvantages were indicated.
1Abstract-Neural network ensemble is an approach based on cooperative usage of many neural networks for problem solving. Often this approach enables to solve problem more efficiently than approach where only one network is used. The two major stages of the neural network ensemble construction are: design and training component networks, combining of the component networks predictions to produce the ensemble output. In this paper, a probability-based method is proposed to accomplish the first stage. Although this method is based on the genetic algorithm, it requires fewer parameters to be tuned. A method based on genetic programming is proposed for combining the predictions of component networks. This method allows us to build nonlinear combinations of component networks predictions providing more flexible and adaptive solutions. To demonstrate robustness of the proposed approach, its results are compared with the results obtained using other methods.
This article presents the development of a method for identifying non-standard errors in control the technological process of maintenance petroleum equipment using intelligent methods. There is stated a new formulation of the technological process control problem for the maintenance of petroleum equipment as the task of classifying errors introduced by means of measuring the parameters of the technological process. Intellectual methods have proven themselves as a tool for solving the problem of classification. Various intellectual methods were considered for solving the classification problem: decision trees, artificial neural networks and fuzzy logic method. Effectiveness comparison of the proposed methods is carried out on the basis of experimental data on actual technological processes of petroleum equipment maintenance. The results of the study indicate that the method based on artificial neural networks is the most efficient in solving the problem of classifying the errors of measuring instruments. The proposed method of managing the technological process of petroleum equipment maintenance is intended primarily to improve the repair quality of such equipment components as the pipeline system for transferring hydrocarbon raw materials. Using the proposed method will improve the quality of maintenance work; improve the durability of the pipeline system, which in turn can increase the efficiency of hydrocarbon production.
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