Multivariate Nonlinear Regression Model of Laser Fusion in vitro Skin Tissue Incision Performance Based on Response Surface Methodology

2018 ◽  
Vol 45 (8) ◽  
pp. 0807002 ◽  
Author(s):  
刘其蒙 Liu Qimeng ◽  
黄俊 Huang Jun ◽  
王克鸿 Wang Kehong ◽  
周琦 Zhou Qi
Keyword(s):  
Response Surface Methodology ◽  
Regression Model ◽  
Response Surface ◽  
Nonlinear Regression ◽  
Skin Tissue ◽  
Laser Fusion ◽  
Nonlinear Regression Model ◽  
Multivariate Nonlinear Regression

scholarly journals Multivariate Quadratic Nonlinear Regression Model of the Ultimate Pull-Out Load of Electrohydraulic Expansion Joints Based on Response Surface Methodology

Coatings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 689
Author(s):  
Da Cai ◽  
Chenyu Jin ◽  
Jie Liang ◽  
Guangyao Li ◽  
Junjia Cui
Keyword(s):  
Response Surface Methodology ◽  
Regression Model ◽  
Response Surface ◽  
Process Parameters ◽  
Nonlinear Regression ◽  
Discharge Voltage ◽  
Wire Diameter ◽  
Nonlinear Regression Model ◽  
Wire Length ◽  
Pull Out

Electrohydraulic expansion joining has great potential for joining the light weight and high strength thin-walled pipes due to its high strain rate. Based on the central composite design (CCD) of response surface methodology, multiple experiments of electrohydraulic expansion joining process were performed. The multivariate quadratic nonlinear regression model between process parameters (discharge voltage, wire length, and wire diameter) and the ultimate pull-out load of the joints was established. The results revealed that discharge voltage, wire length and wire diameter all had a significant effect on the ultimate pull-out load. The discharge voltage had the most significant effect. The interaction between the discharge voltage and the wire diameter had a significant effect on the ultimate pull-out load. The optimal parameter combination (discharge voltage = 6 kV, wire length = 10 mm, wire diameter = 0.833 mm) was obtained and verified through the experiments. This study would provide guidance for the choice of the process parameters in real applications.

scholarly journals Study on ASJ Cutting of TC18, Based upon Multivariate Nonlinear Regression and SA-BP-AGA

Materials ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 1902
Author(s):  
Jie Lin ◽  
Xin Zhou ◽  
Hui Zhang ◽  
Fengchao Wang ◽  
Qiwen Xu ◽  
...  
Keyword(s):  
Regression Model ◽  
Nonlinear Regression ◽  
Simulated Annealing Algorithm ◽  
Quality Criterion ◽  
Adaptive Genetic Algorithm ◽  
Nonlinear Regression Model ◽  
Slurry Concentration ◽  
Study Results ◽  
Multivariate Nonlinear Regression ◽  
Kerf Angle

TC18 titanium alloy has been widely applied, but is considered as a difficult machining material. Taking the kerf angle as the quality criterion, this paper studied the cutting performance of TC18 by the use of an abrasive slurry jet (ASJ), based upon multivariate nonlinear regression and SA-BP-AGA. Cutting experiments were carried out according to the Taguchi orthogonal method. The experimental factors included traverse speed, standoff distance, pressure and slurry concentration, with five levels set, respectively. Meanwhile, a characterization method of the major influencing factors was proposed. A multiple nonlinear regression model and a back propagation artificial neural network (BP) prediction model, based on adaptive genetic algorithm (AGA), were established. The reliability was verified by statistics equations for the 22 groups of the fitting or training model and the three groups of experimental results. The BP-AGA and Simulated annealing algorithm (SA) were used to form a set of prediction optimization systems, called integrated SA-BP-AGA. Finally, the results showed that the main factor influencing the kerf angle is the slurry concentration. BP-AGA is easier to model, offers better robustness and is more accurate than a multivariate nonlinear regression model. The best kerf angle can be predicted by the integration system. The study results can improve the performance for the machining of TC18 by ASJ.

Analyzing Relationship between Genetic, Geographical Factors and Protein Content of Winter Wheat at Different Regional Scales

2011 ◽  
Vol 84-85 ◽  
pp. 74-80
Author(s):  
Ying Ying Dong ◽  
Ji Hua Wang ◽  
Cun Jun Li ◽  
Ju Hua Luo ◽  
Hui Fang Wang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Regression Model ◽  
Protein Content ◽  
Nonlinear Regression ◽  
Estimation Accuracy ◽  
Nonlinear Regression Model ◽  
Simulation Efficiency ◽  
Geographical Factors ◽  
Multivariate Nonlinear Regression ◽  
The Relationship

According to the important status and practical values of geographical factors and genetic factors in protein content estimation of winter wheat, a method analyzing the relationship between factors and protein content was proposed in this paper. In the method, genetic, geographical factors were chosen as independent variables, multivariate nonlinear regression model was introduced to estimate protein content. Firstly, construct one-dimensional nonlinear regression model based on each factor. Secondly, analyze and select sensitive factors on basis of the above simulations. Finally, couple the selected sensitive factors into multivariate nonlinear regression model for protein content estimation. Winter wheat was chosen as experimental subject. The results showed that, at province scale and county scale, the proposed method could not only effectively improve simulation efficiency, but also enhance whole estimation accuracy.

Semiparametric Generalized Least Squares in the Multivariate Nonlinear Regression Model

1992 ◽  
Vol 8 (2) ◽  
pp. 203-222 ◽  
Author(s):  
Miguel A. Delgado
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Nonlinear Regression ◽  
Nearest Neighbor ◽  
Generalized Least Squares ◽  
Nonlinear Regression Model ◽  
Monte Carlo Experiments ◽  
Multivariate Nonlinear Regression ◽  
Unknown Form ◽  
Asymptotically Efficient

Asymptotically efficient estimates for the multiple equations nonlinear regression model are obtained in the presence of heteroskedasticity of unknown form. The proposed estimator is a generalized least squares based on nonparametric nearest neighbor estimates of the conditional variance matrices. Some Monte Carlo experiments are reported.

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