Using artificial neural network on flight distance prediction for paper plane

Abstract

This study shows how to apply the essential principles of paper plane on the flight experiment. It applies three basic structures of paper plane including nose, wing, and rudder movements to predict the optimal flight distance. This study employs the artificial neural network model for data analysis and prediction. The data collected from the flight distance of paper plane are calculated and analyzed in the artificial neural network model in order to predict the optimal flight distance of paper plane. This study is intended to discover the essential principles and basic structures of manufacturing paper plane and provide theoretical and practical contributions in hands-on experiments and aviation science.