An optimization methodology for machine learning strategies and regression problems in ballistic impact scenarios
In domains with limited data, such as ballistic impact, prior researches have proven that the optimization of artificial neural models is an efficient tool for improving the performance of a classifier based on MultiLayer Perceptron. In addition, this research aims to explore, in the ballistic domain, the optimization of other machine learning strategies and their application in regression problems. Therefore, this paper presents an optimization methodology to use with several approaches of machine learning in regression problems, maximizing the limited dataset and locating the best network topology and input vector of each network model. This methodology is tested in real regression scenarios of ballistic impact with different artificial neural models, obtaining substantial improvement in all the experiments. Furthermore, the quality stage, based on criteria of information theory, enables the determination of when the complexity of the network design does not penalize the fit over the data and thereby the selection of the best neural network model from a series of candidates. Finally, the results obtained show the relevance of this methodology and its application improves the performance and efficiency of multiple machine learning strategies in regression scenarios.
optimization methodology machine learning ballistic impacts neural networks genetic algorithm