Abstract
In this paper the use of metamodels to approximate the reverse of simulation models is explored. This purpose of the approach is to achieve the opposite of what a simulation model can do. That is, given a set of desired performance measures, the metamodels output a design to meet management goals. The performance of several neural network simulation metamodels was compared to the performance of a stepwise regression metamodel in terms of accuracy. It was found that in most cases, neural network metamodels outperform the regression metamodel. It was also found that a modular neural network performed the best in terms of minimizing the error of prediction.
Original language | English (US) |
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Pages (from-to) | 537-542 |
Number of pages | 6 |
Journal | Winter Simulation Conference Proceedings |
Volume | 1 |
DOIs | |
State | Published - 1999 |
Event | 1999 Winter Simulation Conference Proceedings (WSC) - Phoenix, AZ, USA Duration: Dec 5 1999 → Dec 8 1999 |
All Science Journal Classification (ASJC) codes
- Software
- Safety, Risk, Reliability and Quality
- Applied Mathematics
- Chemical Health and Safety
- Modeling and Simulation