GAMLSS and neural networks in combat simulation metamodelling: A case study
| Autor: | Trevor J. Ringrose, Petros Boutselis |
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| Rok vydání: | 2013 |
| Předmět: |
Scale (ratio)
Artificial neural network Computer science business.industry General Engineering Prediction interval Context (language use) Combat simulation Machine learning computer.software_genre Regression Computer Science Applications Metamodeling Artificial Intelligence Data mining Artificial intelligence business Additive model computer |
| Zdroj: | Expert Systems with Applications. 40:6087-6093 |
| ISSN: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2013.05.023 |
| Popis: | The GAMLSS (Generalised Additive Models for Location, Scale and Shape) regression approach is compared to neural networks in the context of modelling the relationship between the inputs and outputs of the stochastic combat simulation model SIMBAT. The similarities and differences in these modelling approaches, and their advantages and disadvantages in this case, are discussed. Comparison of out-of-sample prediction suggests that some GAMLSS models are better able to cope with skewed data, but otherwise performance is broadly similar. |
| Databáze: | OpenAIRE |
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