Optimization and response surfaces: Gaussian radial basis functions for simulation metamodeling

Authors:
Miyoung Shin;Robert G. Sargent;Amrit L. Goel
Affiliations:
Electronics and Telecommunication, Research Institute, Yusong-Gu, Taejon;Syracuse University, Syracuse, NY;Syracuse University, Syracuse, NY
Venue:
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
Year:
2002

Citing 3
Cited 3

Elements of artificial neural networks

Elements of artificial neural networks
Research issues in metamodeling

WSC '91 Proceedings of the 23rd conference on Winter simulation
Empirical Data Modeling in Software Engineering Using Radial Basis Functions

IEEE Transactions on Software Engineering

Two new subjective validation methods using data displays

WSC '05 Proceedings of the 37th conference on Winter simulation
A systematic comparison of metamodeling techniques for simulation optimization in Decision Support Systems

Applied Soft Computing
Modeling the leadership - project performance relation: radial basis function, Gaussian and Kriging methods as alternatives to linear regression

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel approach for developing simulation metamodels using Gaussian radial basis functions. This approach is based on some recently developed mathematical results for radial basis functions. It is systematic, explicitly controls the underfitting and overfitting tradeoff, and uses a fast computational algorithm that requires minimal human involvement. This approach is illustrated by developing metamodels for the M/M/1 queueing system.