A.V. Kinarskiy - Post-graduate Student, Dept. of Electronics and Biomedical Technology, Ufa State Aviation Technical University. E-mail: kinarskiy@yandex.ru S.V. Zhernakov - Dr.Sci. (Eng.), Professor, Head, Dept. of Electronics and Biomedical Technology, Ufa State Aviation Technical University. E-mail: zhsviit@mail.ru

The imperfections of currently existing algorithms and software, carrying out the process of aviation gas turbine engine parameters debugging, is analyzed. The authors propose the approach to debugging of aviation gas turbine engine (GTE) parameters based on the neural network (NN) technology. The applying of the neural network technology is effective for solution of hardly-formalizable tasks connected with mode classification, identification, control, diagnostic and forecasting of GTE operating conditions and debugging of his parameters. The result of this research is the engineering technique for parametric debugging of aviation GTE. The final results of this problem solution show: - the engine parameters debugging process is easily formalized at neural network basis; - to calculate the required jet nozzle diameter value, the adjusting graph, based on neural network training by the results of aviation engines fleet bench testing, can be used.