Modified Neural Network Method for Classifying the Helicopters Turboshaft Engines Ratings at Flight Modes

EasyChair Preprint 8902

Abstract

This work is devoted to the modification of neural network method for classifying the helicopters turboshaft engines ratings at flight modes using neural network technologies, which, through the use of a new hybrid network of ART-1 and BAM, made it possible to improve the quality of recognition of operating modes to almost 100 %. The hybrid network ART-1 and BAM training process was modified, which made it possible to adapt the network without adding a new class and train it to recognize existing classes when the incoming data only slightly differs from those recorded in long-term memory. This makes it possible to associate non-identical data with one identifier vector, which makes it possible, when using the classifier in helicopters turboshaft engines automatic control system, to correctly respond to the presented data.

Keyphrases: Classifying, Ratings, helicopters turboshaft engines, neural network, training

@booklet{EasyChair:8902,
  author    = {Serhii Vladov and Yurii Shmelov and Ruslan Yakovliev},
  title     = {Modified Neural Network Method for Classifying the Helicopters Turboshaft Engines Ratings at Flight Modes},
  howpublished = {EasyChair Preprint 8902},
  year      = {EasyChair, 2022}}