Surrogate models composed of locally estimated neural networks
2008 (English)Report (Other academic)
When a computer code model is computationally expensive, or there is a strong demand for short execution times, it may be advantageous to invest in a computationally cheaper surrogate model that can provide almost the same output(s) as the original model. We examined the performance of surrogate models derived by first applying an adaptive or non-adaptive algorithm to generate a set of design points, and subsequently using locally estimated artificial neural networks (ANNs) to predict the output at previously untried inputs. We found that such surrogate models generally performed well, and indeed often much better than ANNs fitted to all data in the entire input domain. Furthermore, we observed that locally estimated ANNs can adapt to response surfaces exhibiting extreme features like sharp ridges, and that such prediction models can accommodate relatively high-dimensional inputs.
Place, publisher, year, edition, pages
, Report-LiU-IDA-STAT, 2
Artificial neural networks, response surface, experimental design, surrogate models, local fitting
Computer and Information Science
IdentifiersURN: urn:nbn:se:liu:diva-17112OAI: oai:DiVA.org:liu-17112DiVA: diva2:201977