Determination of robust weights hidden layers on backpropagation algorithm to analyze coefficient drag high-speed train

In this research, we developed a method using backpropagation to analyze the
coefficient drag of high-speed trains. Analyzing coefficient drag using a backpropagation
algorithm has more benefits, especially in cost and time, than using computational fluid
dynamics. Computational fluid dynamics need sophisticated software and hardware. It needs
much time to get a convergent result as well. We used 2D coordinates longitudinal profile nose
of the high-speed train as an input of the backpropagation algorithm. The weights between
hidden layers and input layers and between hidden layers and output layers, respectively, were
modeled as matrices that were formed from the iteration process. The coefficient drag
differences, between backpropagation algorithm and computational fluid dynamics analysis,
from each iteration, were used as a correction factor to form robust weights hidden layers
matrices. The results of this research showed that training in the backpropagation algorithm
can obtain robust weights of hidden layers that have been known from Mean Sum Square Error in an exercise that is small enough. Because of the limited time to finish this research, we only trained and exercised nine models instead of a thousand models. Robustness weights that are resulted in this research are expected to contribute to accelerating a coefficient drag prediction of high-speed train accurately. To improve this proposed method, 3D coordinates of the nose’s surface of high-speed trains and many more 3D models are needed.