Solid Rocket Motor Design-Classification Using a Genetic-Algorithm-Optimized Neural Network Ensemble

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.