NEUROMODELING OF OPERATIONAL PROCESSES

Abstract

Context. The problem of synthesis a neural network model of operational processes with the determination of the optimal topology, which is characterized by a high level of logical transparency and acceptable accuracy, is considered. The object of the study is the process of neural network modeling of operational processes using an indicator system to simplify the selection of the topology of neuromodels. 
 Objective of the work is to synthesis a neural network model of operational processes with a high level of logical transparency and acceptable accuracy based on the use of an indicator system. 
 Method. It is proposed to use a system of indicators to determine the topological features of ANN, which is the basis for modeling operational processes. The assessment of the level of complexity of the task obtained on the basis of information about the input data and the values of the criteria for assessing the specificity of the task allows to categorize the task to one of the types of complexity in order to determine the approach to the synthesis of a neuromodel. Complexity category OS allows, based on analytical data about the selection of input data, to obtain the exact number of neurons in the hidden layer for the synthesis of a neuromodel with a high level of logical transparency, which significantly expands their practical use and reduces the cost of subsequent operational processes. 
 Results. The obtained neuromodels of operational processes based on historical data. The use of the indicator system made it possible to significantly increase the level of logical transparency of the models, while maintaining high accuracy. Synthesized neuromodels reduce the resource intensity of operational processes by increasing the level of previous modeling. 
 Conclusions. The conducted experiments confirmed the operability of the proposed mathematical software and allow to recommend it for use in practice when modeling operational processes. The prospects for further research may consist in the use of more complex methods of feature selection to fix the group relationships of information features for the construction of more complex models