scholarly journals A multivariate nonlinear regression model for the resistance power of a light rail vehicle

2021 ◽  
Vol 8 (1) ◽  
pp. 33-38
Author(s):  
Mine SERTSÖZ ◽  
Mehmet FİDAN
Keyword(s):  
Regression Model ◽  
Nonlinear Regression ◽  
Light Rail ◽  
Nonlinear Regression Model ◽  
Rail Vehicle ◽  
Multivariate Nonlinear Regression

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.

scholarly journals Dimension Analysis-Based Model for Prediction of Shale Compressive Strength

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangyu Fan ◽  
Fenglin Xu ◽  
Lin Chen ◽  
Qiao Chen ◽  
Zhiwei Liu ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Prediction Error ◽  
Diameter Ratio ◽  
Nonlinear Regression Model ◽  
Average Prediction Error ◽  
Model Average ◽  
Dimension Analysis ◽  
Longmaxi Shale

The compressive strength of shale is a comprehensive index for evaluating the shale strength, which is linked to shale well borehole stability. Based on correlation analysis between factors (confining stress, height/diameter ratio, bedding angle, and porosity) and shale compressive strength (Longmaxi Shale in Sichuan Basin, China), we develop a dimension analysis-based model for prediction of shale compressive strength. A nonlinear-regression model is used for comparison. A multitraining method is used to achieve reliability of model prediction. The results show that, compared to a multi-nonlinear-regression model (average prediction error = 19.5%), the average prediction error of the dimension analysis-based model is 19.2%. More importantly, our dimension analysis-based model needs to determine only one parameter, whereas the multi-nonlinear-regression model needs to determine five. In addition, sensitivity analysis shows that height/diameter ratio has greater sensitivity to compressive strength than other factors.

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