Neural Network Method for Helicopters Turboshaft Engines Working Process Parameters Identification at Flight Modes
Serhii Vladov, Yurii Shmelov, Ruslan Yakovliev, Marina Petchenko, Svitlana Drozdova
Abstract
This work is devoted to the development of a neural network method for helicopters turboshaft engines working process thermo-gas-dynamic parameters identification. The developed method is based on a neural network - a three-layer perceptron of architecture 7-53-36, the optimal training method for which - back propagation algorithm. To solve the applied problem of identification of helicopter turboshaft engines (using the example of an aircraft engine TV3-117). The error of identification of thermo-gas-dynamic parameters of the working process of turboshaft helicopter using the developed three-layer perceptron does not exceed 0.68 %. For the classical method (thermo-gas-dynamic model of helicopter turboshaft engine) it is about 2 % in the considered range of changes in the engine operating mode.