An Artificial Neural Network Based Metamodel for Analysing a Stochastic Combat Simulation
An Artificial Neural Network Based Metamodel for Analysing a Stochastic Combat Simulation
复制标题
用于分析随机战斗模拟的基于人工神经网络的元模型
DOI：
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发表时间：
2006
影响因子：
1.2
通讯作者：
T. Ringrose
中科院分区：
文献类型：
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作者：
Fasihul M. Alam;K. McNaught;T. Ringrose
在本文中，我们讨论了使用多层感知器（MLP）神经网络预测随机作战仿真模型的输出。使用更快的辅助模型来近似复杂仿真模型的输入-输出关系在仿真社区中被称为元建模。由于许多仿真模型包含大量的输入参数，然而，有必要减少所考虑的集合，并确定在给定特定建模上下文的元模型中包含的最重要的参数。本文采用两阶段实验设计-首先，莫里斯随机一次（OAT）设计被用作因子筛选方法，然后采用拉丁超立方体设计来选择定义模拟模型所需运行的输入配置集。这组输入配置与相关的仿真输出一起为MLP网络的开发提供了训练数据。该方法示出了参考称为SIMBAT的随机作战模拟。然后，本文研究了一些方面的发展基于神经网络的随机作战模拟的元模型。它表明，使用随机模拟的每次重复的输出通常比仅使用平均输出更好。
In this paper, we discuss the use of multi-layer perceptron (MLP) neural networks to predict the outputs from a stochastic combat simulation model. The use of a faster, auxiliary model to approximate the input-output relationships of a complex simulation model is known in the simulation community as metamodelling. Since many simulation models contain a large number of input parameters, however it is necessary to reduce the set considered and determine the most important ones to include in a metamodel given a particular modelling context. This paper employs a two-stage experimental design — first, Morris’ randomised one-at-a-time (OAT) design is used as a factor screening method, and then a Latin Hypercube design is employed to select the set of input configurations which defines the required runs of the simulation model. The set of input configurations together with the associated simulation outputs provide the training data for the development of the MLP networks. The approach is illustrated with reference to a stochastic combat simulation called SIMBAT. The paper then investigates a number of aspects relating to the development of neural network-based metamodels of stochastic combat simulations. It shows that using the outputs from each replication of a stochastic simulation is generally better than only using the mean output.