What is it about?
In order to quickly and accurately evaluate the rudder's performance and screen for faults, we add the machine learning function to the traditional automatic testing equipment, which saves a lot of manpower and time in the testing work.
Photo by SpaceX on Unsplash
Why is it important?
Our study not only break through the shortcomings of traditional rudder testing methods and technical bottleneck of low parameter testing efficiency, but also propose a new optimized decision tree algorithm (SFLA-MWDT) which solves the common decision difficulty in tree models caused by low-precision decision and high-vote competition.
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This page is a summary of: Performance evaluation and prediction of rudders based on machine learning technology, Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering, June 2019, SAGE Publications, DOI: 10.1177/0954410019857380.
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