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A Study On The Nonparametric Response Surface Methodology And Its Application

Posted on:2005-06-25Degree:MasterType:Thesis
Country:ChinaCandidate:Y X XiaoFull Text:PDF
GTID:2156360122987522Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
Response surface methodology (RSM) is used to get the optimal value of unknown response surface.To get a good response surface is very important to find the optimal value. But the surface is often more complex than that we can imagine. The classical RSM will yield a local optimal result, for the sake of the location of the orginal center point. Sometimes the polynomial fitting cannot reflect the high order interaction between the infleunce factors. Nonparametric response surface methodology (NPRSM) is a good tool to deal with the complex problem and can conquer the shortcomings of classical RSM metioned above.The thesis describes the three phases of RSM, which is DOE, data fitting and searching for the optimal value, and tries to make NPRSM analysis in the same way. Firstly, the design of space-filling grid is fit for the nonparametric regression. The nonparametric regression fits the surface well by using the space-filling grid and can easily find the optimal area. Secondly, a regression-based intepolation artificial neural network is developed to fit the optimum area. The regression-based intepolation artificial neural network is steady, precise, and fits complex unknown surface without overfitting like any other usual neural networks. The thesis presents a mulit-phase NPRSM, which can use DOE, polynomial fitting, nonparametric regression, and neural network in different conditions when it is need. At the end of the thesis, two examples are analyzed by the mulit-phase NPRSM and they prove that the method is very effective.
Keywords/Search Tags:Resoponse Surface Methodology, Nonparametric Regression, Artificial Neural Networks, NPRSM
PDF Full Text Request
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