Detection of dynamic errors in aircraft flight data

Abstract

The paper considers the algorithmic and methodologic support for detecting dynamic errors in aircraft on-board measurements using parameter identification and system approach to flight data analysis. Examples of practical application of the considered methods and algorithms for detecting dynamic errors in modern aircraft flight data are presented.

Estimation of aerodynamic parameters using neural artificial bee colony fusion algorithm for moderate angle of attack using real flight data

“…Initially, an efficient and appropriate neural structure is created that can manage the associated non-linearities while handling large datasets using a vector consisting of the input variable Ip(µ) and output variable Zp(µ+1) for both longitudinal and lateral dynamics. 40 The neural network performance, which is its ability to duplicate the training data accurately, depends on the size of the networks, the number of hidden layers, and the number of neurons in hidden layers.…”