TY - GEN
T1 - Multivariate polynomial response surface analysis - Combining advantages of multilinear regression and artificial neural networks
AU - Vaccari, David A.
N1 - Publisher Copyright:
© 2018 Proceedings of the IASTED International Conference on Modelling, Simulation and Identification, MSI 2018. All rights reserved.
PY - 2018
Y1 - 2018
N2 - A novel approach is described for empirically modeling multivariate response surfaces, either time-series or non-time series. The approach uses multivariate polynomial regression (MPR) with a step-wise algorithm to select terms. The approach includes advantages of multilinear regression such as simplicity and transparency. It also has the advantages of more complex modeling approaches such as artificial neural networks (ANNs) it its ability to model complex response surfaces, including high degree curvilinear interactions. Furthermore, MPR has advantages over ANNs in its transparency, tractability, parsimony and resistance to overfitting. These advantages are illustrated by an example and a freely-available online tool for fitting these models is described. Despite similarities to Response Surface Methodology (RSM), the models produced by this method tend to be different. An example response surface analysis is used to illustrate these characteristics.
AB - A novel approach is described for empirically modeling multivariate response surfaces, either time-series or non-time series. The approach uses multivariate polynomial regression (MPR) with a step-wise algorithm to select terms. The approach includes advantages of multilinear regression such as simplicity and transparency. It also has the advantages of more complex modeling approaches such as artificial neural networks (ANNs) it its ability to model complex response surfaces, including high degree curvilinear interactions. Furthermore, MPR has advantages over ANNs in its transparency, tractability, parsimony and resistance to overfitting. These advantages are illustrated by an example and a freely-available online tool for fitting these models is described. Despite similarities to Response Surface Methodology (RSM), the models produced by this method tend to be different. An example response surface analysis is used to illustrate these characteristics.
KW - Empirical
KW - Modeling
KW - Multivariate
KW - Polynomial
UR - http://www.scopus.com/inward/record.url?scp=85092017284&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092017284&partnerID=8YFLogxK
U2 - 10.2316/P.2018.857-021
DO - 10.2316/P.2018.857-021
M3 - Conference contribution
AN - SCOPUS:85092017284
T3 - Proceedings of the IASTED International Conference on Modelling, Simulation and Identification ,MSI 2018
SP - 23
EP - 30
BT - Proceedings of the IASTED International Conference on Modelling, Simulation and Identification ,MSI 2018
T2 - 2018 IASTED International Conference on Modelling, Simulation and Identification ,MSI 2018
Y2 - 16 July 2018 through 17 July 2018
ER -