The past decade has seen an explosion of machine learning research and appli-
cations; especially, deep learning METHODS have enabled key advances in many
applicationdomains,suchas computervision,speechprocessing,andgameplaying.
However, the performance of many machine learning METHODS is very sensitive
to a plethora of design decisions, which constitutes a considerable barrier for
new users. This is particularly true in the booming field of deep learning, where
human engineers need to select the right neural architectures, training procedures,
regularization METHODS, and hyperparameters of all of these components in order to
make their networks do what they are supposed to do with sufficient performance.
This process has to be repeated for every application. Even experts are often left
with tedious episodes of trial and error until they identify a good set of choices for
a particular dataset.
METHODS for designing a maintenance simulation training system for certain kind of radio are introduced. Fault modeling method is used to establish the fault database. The system sets up some typical failures， follow the prompts trainers can locate the fault source and confirm the type to accomplish corresponding fault maintenance training. A training evaluation means is given to examining and evaluating the training performance. The system intuitively and vividly shows the fault maintenance process， it can not only be used in teaching， but also in daily maintenance training to efficiently improve the maintenance operation level. Graphical programming language LabVIEW is used to develop the system platform.
