Name
Abstract | ||
|---|---|---|
Metamodeling methods have been widely used in engineering applications to create surrogate models for complex systems. In the past, the input–output relationship of the complex system is usually approximated globally using only a single metamodel. In this research, a new metamodeling method, namely multi-surrogate approximation (MSA) metamodeling method, is developed using multiple metamodels when the sample data collected from different regions of the design space are of different characteristics. In this method, sample data are first classified into clusters based on their similarities in the design space, and a local metamodel is identified for each cluster of the sample data. A global metamodel is then built using these local metamodels considering the contributions of these local metamodels in different regions of the design space. Compared with the traditional approach of global metamodeling using only a single metamodel, this MSA metamodeling method can improve the modeling accuracy considerably. Applications of this metamodeling method have also been demonstrated in this research. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s00366-009-0173-y | Eng. Comput. (Lond.) |
Keywords | Field | DocType |
sample data,msa metamodeling method,metamodeling method,design space,multi-surrogate approximation method,complex system,different region,single metamodel,local metamodels,new metamodeling method,global metamodeling,metamodelingmultivariate polynomial � radial basis functionskrigingbayesian neural network � gaussian mixture model,data collection,input output,neural network,gaussian mixture model | Kriging,Design space,Complex system,Mathematical optimization,Radial basis function,Bayesian neural networks,Artificial intelligence,Metamodeling,Mixture model,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 2 | 1435-5663 |
Citations | PageRank | References |
2 | 0.54 | 4 |
Authors | ||
2 | ||