GAMLSS and neural networks in combat simulation metamodelling: A case study

Authors:
P. Boutselis;T. J. Ringrose
Affiliations:
Hellenic Navy General Staff, 229 Mesogion Av., 15561 Cholargos, Athens, Greece;Department of Informatics and Systems Engineering, Cranfield University, Defence Academy of the United Kingdom, Shrivenham, Swindon SN6 8LA, United Kingdom
Venue:
Expert Systems with Applications: An International Journal
Year:
2013

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Abstract

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.