Name
Title | ||
|---|---|---|
Dealing with limited data in ballistic impact scenarios: an empirical comparison of different neural network approaches |
Abstract | ||
|---|---|---|
In the domain of high-speed impact between solids, the simulation of one trial entails the use of large resources and an elevated
computational cost. The objective of this research is to find the best neural network associated with a new problem of ballistic
impact, maximizing the quantity of trials available and simplifying their architecture. To achieve this goal, this paper proposes
a tuning performance process based on four stages. These stages include existing statistical techniques, a combination of
proposals to improve the performance and analyze the influence of each variable. To measure the quality of the different networks,
two criteria based on information theory have been incorporated to reflect the fit of the data with respect to their complexity.
The results obtained show that the application of an integrated tuning process in this domain permits improvement in the performance
and efficiency of a neural network in comparison with different machine learning alternatives |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s10489-009-0205-8 | Applied Intelligence |
Keywords | Field | DocType |
Neural network,Limited data,Bootstrapping,Cross-validation,Ballistic impact,Numerical simulation,Machine learning classifiers | Information theory,Empirical comparison,Data mining,Architecture,Computer simulation,Computer science,Bootstrapping,Ballistic impact,Artificial intelligence,Artificial neural network,Cross-validation,Machine learning | Journal |
Volume | Issue | ISSN |
35 | 1 | 0924-669X |
Citations | PageRank | References |
8 | 0.64 | 37 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Israel Gonzalez-Carrasco | 1 | 70 | 7.19 |
| Angel Garcia-Crespo | 2 | 57 | 6.83 |
| Belen Ruiz-Mezcua | 3 | 44 | 3.68 |
| Jose Luis Lopez-Cuadrado | 4 | 54 | 5.53 |