The article’s main provisions are the development and application of a neural network method for helicopter turboshaft engine thermogas-dynamic parameter integrating signals. This allows you to effectively correct sensor data in real time, ensuring high accuracy and reliability of readings. A neural network has been developed that integrates closed loops for the helicopter turboshaft engine parameters, which are regulated based on the filtering method. This made achieving almost 100% (0.995 or 99.5%) accuracy possible and reduced the loss function to 0.005 (0.5%) after 280 training epochs. An algorithm has been developed for neural network training based on the errors in backpropagation for closed loops, integrating the helicopter turboshaft engine parameters regulated based on the filtering method. It combines increasing the validation set accuracy and controlling overfitting, considering error dynamics, which preserves the model generalization ability. The adaptive training rate improves adaptation to the data changes and training conditions, improving performance. It has been mathematically proven that the helicopter turboshaft engine parameters regulating neural network closed-loop integration using the filtering method, in comparison with traditional filters (median-recursive, recursive and median), significantly improve efficiency. Moreover, that enables reduction of the errors of the 1st and 2nd types: 2.11 times compared to the median-recursive filter, 2.89 times compared to the recursive filter, and 4.18 times compared to the median filter. The achieved results significantly increase the helicopter turboshaft engine sensor readings accuracy (up to 99.5%) and reliability, ensuring aircraft efficient and safe operations thanks to improved filtering methods and neural network data integration. These advances open up new prospects for the aviation industry, improving operational efficiency and overall helicopter flight safety through advanced data processing technologies.

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The approach to the development of intelligent decision support systems using ontology knowledge bases consisting of such systems in the article is considered. The classification of such systems in terms of their operation based on ontologies is carried out. The mathematical functioning of intelligent decision support systems and intelligent search system based on ontology is developed. The notion of adaptive ontologies is introduced. An adaptive ontology is proposed to define as an ontology with concepts and relations weighted according to its importance for a given subject domain. This model of ontology defines not only explicit, but also implicit knowledge. The mathematical functioning of intelligent decision support systems using adaptive ontologies formalizes decision making of such a system. Semantic metric based on adaptive ontologies was built, unlike other metrics, takes into account the causal relation between the concepts, not just their taxonomy. For feature problems mathematical software based on automated determination of properties of set concepts was developed, according to the values which the process of decision support was carried out.