Support Vector Machines for Characterising Whipple Shield Performance

Abstract

Support Vector Machines (SVMs) are a classification technique used in data mining and machine learning that are particularly well suited for application with sparse data sets. A database of over 1100 hypervelocity impact tests using spherical aluminium projectiles against spaced aluminium armour (i.e. Whipple shield) was compiled and used to train four different SVMs. The SVMs were developed using a variety of input-attributes and Principal Component Analysis (PCA). Initially, a maximum accuracy of 75% was obtained for an SVM when applied to predict the perforated/not-perforated outcome of impact events not included in the training process. A number of tests were identified which were incons..