IMPLEMENTATION OF THE INDICATOR SYSTEM IN MODELING OF COMPLEX TECHNICAL SYSTEMS
DOI:
https://doi.org/10.15588/1607-3274-2021-1-12Keywords:
complexity assessment, indicator system, modeling, neuromodel, sampling, training, error, gradient.Abstract
Context. The problem of determining the optimal topology of a neuromodel, which is characterized by a high level of logical transparency in modeling complex technical systems, is considered. The object of research is the process of applying an indicator system to simplify and select the topology of neuromodels.
Objective of the work is to develop and use a system of indicators to determine the level of complexity of the modeling problem and gradually select the optimal logically transparent topology of the neuromodel.
Method. A method is proposed for selecting an optimal, logically transparent neural network topology for modeling complex technical systems using a system of corresponding indicators. At the beginning, the method determines the overall level of complexity of the modeling task and, using the obtained estimate, determines the method for further optimization of the neuromodel. Then, using Task data and input data characteristics, the method allows to obtain the most optimal structure of the neural model for further modeling of the system. The method reduces trainingvtime and increases the level of logical transparency of neuromodels, which significantly expands the practical use of such models, without using neuroevolution methods, which may not be justified by resource-intensive tasks.
Results. The developed method is implemented and investigated in solving the problem of modeling the dynamics of pitting processes of steel alloys. Using the developed method made it possible to reduce the training time of the model by 22%, depending on the computing resources used. The method also increased the level of logical transparency of the model by reducing the number of computing nodes by 50%, which also indicates faster and more efficient use of resources.
Conclusions. The conducted experiments confirmed the operability of the proposed mathematical support and allow us to recommend it for use in practice in the design of topologies of neuromodels for further solving modeling, diagnosis and evaluation problems. Prospects for further research may consist in the development of methods for structural optimization of previously synthesized models and the development of new methods for feature selection.
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