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Solid Rocket Motor Design-Classification Using a Genetic-Algorithm-Optimized Neural Network Ensemble
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DOI：10.2514/1.i011730.png)
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Abstract
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Ensemble deep-learning methods are developed to swiftly differentiate between similar solid rocket motor variants using early-flight trajectory data. Two classes of rockets were defined, and fly-out data were generated using a 6-DOF code. Three studies were conducted, each with varying levels of similarity between the two classes. The individual model architectures were optimized with a genetic algorithm, and comparisons were made with unoptimized (weaker learning) ensembles. Ensembles consisting of optimized models achieved a few percent increase in classification accuracy over the best individual model accuracies.
Keywords:
Artificial Neural Network
Rocket Design
Genetic Algorithm
Linear Discriminant Analysis
Short Range Ballistic Missile
Numerical Integration
Neural Network Methods
Radar Applications
Journal
IF:
1.5
Papers:
72
Citations:
987
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Cited Papers
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