Accession Number:

ADA285951

Title:

Artificial Neural Network Metamodels of Stochastic Computer Simulations

Descriptive Note:

Final rept. 25 Jun 1990-10 Aug 1994

Corporate Author:

PITTSBURGH UNIV PA DEPT OF INDUSTRIAL ENGINEERING

Personal Author(s):

Report Date:

1994-08-10

Pagination or Media Count:

254.0

Abstract:

A computer simulation model can be thought of as a relation that connects input parameters to output measures. Since these models can become computationally expensive in terms of processing time andor 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 aggregationreduction. Artificial Neural Networks, Computer Simulation Metamodel, Regression, Response Surface Methods, Simulation Optimization.

Subject Categories:

  • Computer Programming and Software

Distribution Statement:

APPROVED FOR PUBLIC RELEASE