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Artificial Neural Network Metamodels of Stochastic Computer Simulations

机译：随机计算机模拟的人工神经网络模型

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摘要

A computer simulation model can be thought of as a relation that connects inputparameters to output measures. Since these models can become computationally expensive in terms of processing time and/or memory requirements, there are many reasons why it would be beneficial to be able to approximate these models in a computationally expedient manner. This research examines the use of artificial neural networks (ANN), to develop a metamodel of computer simulations. The development and use of the Baseline ANN Metamodel Approach is provided and is shown to outperform traditional regression approaches. The results provide a solid foundation and methodological direction for developing ANN metamodels to perform complex tasks such as simulation optimization, sensitivity analysis, and simulation aggregation/reduction. Artificial Neural Networks, Computer Simulation Metamodel, Regression, Response Surface Methods, Simulation Optimization.

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作者
Kilmer, R. A.;
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年度 1994
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总页数 254
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Computerized simulation; Neural nets; Approach; Computers; Input; Models; Optimization; Output; Parameters; Requirements; Response; Surfaces; Artificial intelligence; Regression analysis; Stochastic processes;

机译：计算机模拟;神经网络;方法;计算机;输入;模型;优化;输出;参数;要求;响应;曲面;人工智能;回归分析;随机过程;

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Artificial Neural Network Metamodels of Stochastic Computer Simulations

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