Two-Stage Response Surface Approaches to Modeling Drug Interaction

2014 ◽  
pp. 90-111
Keyword(s):  
Drug Interaction ◽  
Response Surface ◽  
Two Stage

A Two-Stage Response Surface Approach to Modeling Drug Interaction

2012 ◽  
Vol 4 (4) ◽  
pp. 375-383 ◽  
Author(s):  
Wei Zhao ◽  
Lanju Zhang ◽  
Lingmin Zeng ◽  
Harry Yang
Keyword(s):  
Drug Interaction ◽  
Response Surface ◽  
Two Stage ◽  
Surface Approach

A Two-Stage Optimization Method for the Stencil Printing Process Based on Neural Network and Response Surface Method

2010 ◽  
Vol 156-157 ◽  
pp. 10-17 ◽  
Author(s):  
Er Shun Pan ◽  
Yao Jin ◽  
Zhao Mei ◽  
Ying Wang
Keyword(s):  
Neural Network ◽  
Response Surface ◽  
Response Surface Method ◽  
Model Fitting ◽  
Optimization Method ◽  
Small Sample ◽  
Two Stage ◽  
Stencil Printing ◽  
Printing Process ◽  
Surface Method

A stencil printing process (SPP) optimization problem is studied in this paper. Due to the limitation that neural network requires a large number of samples for the accurate model fitting, a two-stage SPP optimization method is proposed. The design interval can be reduced with small sample by using neural network. In this reduced design interval , response surface method is adopted to obtain the accurate mathematical SPP model. The concept of confidence level is introduced to make the proposed model robust. An interactive method is used to solve the model. The proposed method is compared with the one-stage optimization method and the results show that the proposed method achieves a better performance on each objective.

On Construction of Two-stage Response Surface Designs

2009 ◽  
Vol 6 (4) ◽  
pp. 493-502
Author(s):  
Xuan Lu ◽  
Dennis K. J. Lin ◽  
Daxin Zhou
Keyword(s):  
Response Surface ◽  
Two Stage ◽  
Response Surface Designs

scholarly journals Novel Two-Stage Method for Low-Order Polynomial Model

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Cheng Yan ◽  
Xiuli Shen ◽  
Fushui Guo
Keyword(s):  
Response Surface ◽  
Surface Model ◽  
Two Stage ◽  
Nonlinear Simulation ◽  
Weakly Nonlinear ◽  
Second Stage ◽  
Sampling Points ◽  
Order Polynomial ◽  
Polynomial Response Surface ◽  
Low Order

One of the most popular statistical models is a low-order polynomial response surface model, i.e., a polynomial of first order or second order. These polynomials can be used for global metamodels in weakly nonlinear simulation to approximate their global tendency and local metamodels in response surface methodology (RSM), which has been studied in various applications in engineering design and analysis. The order of the selected polynomial determines the number of sampling points (input combinations) and the resulting accuracy (validity, adequacy). This paper derives a novel method to obtain an accurate high-order polynomial while requiring fewer sampling points. This method uses a two-stage procedure such that the second stage modifies the low-order polynomial estimated in the first stage; this second stage does not require new points. This paper evaluates the performance of the method numerically by using several test functions. These numerical results show that the proposed method can provide more accurate predictions than the traditional method.

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