SYNTHESIS OF A NEURAL NETWORK MODEL OF INDUSTRIAL CONSTRUCTION PROCESSES USING AN INDICATOR SYSTEM
DOI:
https://doi.org/10.15588/1607-3274-2021-4-7Keywords:
modeling, industrial processes, indicator system, neuromodel, sampling, training, error.Abstract
Context. The problem of a neural network model synthesis for industrial processes with the definition of an optimal topology characterized by a high level of logical transparency and acceptable accuracy is considered. The object of research is the process of neural network modeling of industrial processes using an indicator system to simplify and select the topology of neuromodels..
Objective of the work is consists in synthesis a neural network model of industrial processes with a high level of logical transparency and acceptable accuracy based on the use of the system.
Method. A method is proposed to use artificial neural networks of feedforward propagation for modeling industrial processes. After evaluating the overall level of complexity of the modeling problem based on the indicator system, it was decided to build a neuromodel based on historical data. Using the characteristics of the input data of the problem, the most optimal structure of the neural network was calculated for further modeling of the system. A high level of logical transparency of neuromodels significantly expands their practical use and reduces the resource intensity of industrial processes.
Results. Neuromodels of industrial processes are obtained based on historical data. The use of an indicator system made it possible to significantly increase the level of logical transparency of models, while maintaining a high level of accuracy. Constructed neuromodels reduce the resource intensity of industrial processes by increasing the level of preliminary modeling.
Conclusions. The conducted experiments confirmed the operability of the proposed mathematical software and allow us to recommend it for use in practice in modeling industrial processes. Prospects for further research may lie in the neuroevolutionary synthesis of more complex topologies of artificial neural networks for performing multi-criteria optimization.
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