Application of Non-Linear Partial Least-Squares Regression in the Prediction of Carbonization Depth of Concrete

2011 ◽  
Vol 341-342 ◽  
pp. 53-57
Author(s):  
Jian Ping Jiang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Exposure Time ◽  
Latent Variables ◽  
Linear Method ◽  
Forecast Model ◽  
Residual Value ◽  
Least Squares Regression ◽  
Multinomial Regression ◽  
Non Linear

Based on partial least-squares multinomial regression, this paper had a prediction on carbonization depth of conctete. Taking water-cement ratio (i.e. water-ash ratio), cement content (i.e. application amount of cement material), and exposure time of concrete as independent variables , and carbonization depth of conctete as dependent variable , the forecast model of carbonization depth of conctete was obtained. It was found that, Press residual value decreased with the increase of number of latent variables, and the number of latent variables were three by Press residual value versus number of latent variables. The normal regression coefficient of exposure time of concrete was the largest wiithin three influence factors, this indicated that the influence of exposure time of concrete was largest to conctete carbonization depth; The determination coefficient of forecast model obtained in this paper was 0.9940, the error of forecast model was . The following conclusion can be drawn that, the model is accurate and credible, and the partial least-squares multinomial regression is a eximious non-linear method, and it is worthy to spread its application in the forecast analysis of conctete carbonization depth.

Application of Partial Least-Squares Regression in the Prediction of Concrete Strength Taking into Account Interactional Items Among Independent Variables

2011 ◽  
Vol 101-102 ◽  
pp. 220-223
Author(s):  
Jian Ping Jiang
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Partial Least Squares Regression ◽  
Concrete Strength ◽  
Forecast Model ◽  
Least Squares Regression ◽  
Independent Variables ◽  
Cementing Material ◽  
Water Ratio

Based on partial least-squares regression taking into account interactional items among independent variables, this paper had a prediction on concrete strength at the 28th day. Taking proportion of flyash in cementing material, usage amount of cementing material, ash-water ratio as independent variables , and concrete strength at the 28th day as dependent variable , the forecast model of concrete strength was obtained. It was found that press residual value decreased with the increase of number of latent variables, and number of latent variables were three according to Press residual value versus number of latent variables. The normal regression coefficient of ash-water ratio was the largest in three influence factors, this indicated that the influence of ash-water ratio was largest to concrete strength at the 28th day; The determination coefficient of forecast model obtained in this paper was 0.9353, the error of forecast model was. The following conclusion can be drawn that, the model is accurate and credible, and the partial least-squares regression taking into account interactional items among independent variables is a eximious non-linear method, and it is worthy to spread its application in the forecast analysis of concrete strength at the 28th day.

Noise source identification for industrial sewing machines based on non-linear partial least squares regression model

2016 ◽  
Vol 230 (16) ◽  
pp. 2817-2827 ◽  
Author(s):  
Zhi-yong Zhang ◽  
Xin Liu ◽  
Cai-xia Huang ◽  
Da Pan
Keyword(s):  
Regression Model ◽  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Sound Pressure ◽  
Predictive Accuracy ◽  
Least Squares Regression ◽  
Explanatory Variables ◽  
Non Linear ◽  
Response Variables

This paper introduces an application of non-linear partial least squares for vibro-acoustic regression modeling and for an industrial sewing machine. In the vibro-acoustic regression model, the vibration accelerations of reference points are defined as explanatory variables, while the noise sound pressure of target points is defined as response variables, and the number of explanatory variables is determined initially by a correlation analysis in the time domain. To improve predictive accuracy while a non-linear relationship exists between the explanatory and response variables, the explanatory variables are preprocessed by kernel function transformation. The comparison of regressive noise sound pressure to experimental data indicates that the non-linear partial least squares regression model has high predictive accuracy. Furthermore, the contributions of vibration accelerations to noise sound pressure are analyzed, by which the structure optimizations are guided and practiced. The comparison of noise test results before and after optimization testifies to the effectiveness of the contribution analysis.

scholarly journals Multimedia retrieval based on non-linear graph-based fusion and partial least squares regression

2017 ◽  
Vol 76 (21) ◽  
pp. 22383-22403 ◽  
Author(s):  
Ilias Gialampoukidis ◽  
Anastasia Moumtzidou ◽  
Dimitris Liparas ◽  
Theodora Tsikrika ◽  
Stefanos Vrochidis ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Multimedia Retrieval ◽  
Least Squares Regression ◽  
Linear Graph ◽  
Non Linear

scholarly journals Robust Nonlinear Partial Least Squares Regression Using the BACON Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Abdelmounaim Kerkri ◽  
Jelloul Allal ◽  
Zoubir Zarrouk
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Partial Least Squares Regression ◽  
Chemical Engineering ◽  
Simulated Data ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
Computationally Efficient ◽  
The Difference

Partial least squares regression (PLS regression) is used as an alternative for ordinary least squares regression in the presence of multicollinearity. This occurrence is common in chemical engineering problems. In addition to the linear form of PLS, there are other versions that are based on a nonlinear approach, such as the quadratic PLS (QPLS2). The difference between QPLS2 and the regular PLS algorithm is the use of quadratic regression instead of OLS regression in the calculations of latent variables. In this paper we propose a robust version of QPLS2 to overcome sensitivity to outliers using the Blocked Adaptive Computationally Efficient Outlier Nominators (BACON) algorithm. Our hybrid method is tested on both real and simulated data.

Phytoplankton community to assess the environmental status of the Adriatic Sea via non-linear partial least squares regression

2016 ◽  
Vol 51 (2) ◽  
pp. 799-812 ◽  
Author(s):  
Olga Mangoni ◽  
Rosaria Lombardo ◽  
Ida Camminatiello ◽  
Francesca Margiotta ◽  
Augusto Passarelli ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Phytoplankton Community ◽  
Adriatic Sea ◽  
Least Squares Regression ◽  
Environmental Status ◽  
Non Linear

Study on Forecasting Model of Monthly Electricity Consumption Based on Kernel Partial Least-Squares and Exponential Smoothing Method

2013 ◽  
Vol 805-806 ◽  
pp. 1221-1227
Author(s):  
Hai Yan Wang ◽  
Shi Jun Chen
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Electricity Market ◽  
Electricity Consumption ◽  
Forecast Model ◽  
Exponential Smoothing ◽  
Least Squares Regression ◽  
Kernel Partial Least Squares ◽  
Cyclical Trend ◽  
Monthly Electricity Consumption

it is very necessary for electricity market operation to accurate forecasting monthly electricity consumption, influencing factors of electricity consumption, there are non-linear and strong correlation, taking into account the cyclical trend of the monthly electricity consumption, this paper raises a monthly electricity consumption forecast model based on kernel partial least squares and exponential smoothing regression. The forecast model is the first to use kernel partial least squares regression methods to predict the annual electricity consumption, and then combined with exponential smoothing obtained monthly electricity accounts for the proportion of electricity consumption throughout the year for each month of the year to be measured power consumption . Instance analysis and calculation results show that the method has higher prediction accuracy, good practicality and feasibility.

Variation Sharing

2017 ◽  
Vol 8 (4) ◽  
pp. 46-68 ◽  
Author(s):  
Ned Kock ◽  
Shaun Sexton
Keyword(s):  
Measurement Error ◽  
Least Squares ◽  
Partial Least Squares ◽  
Latent Variables ◽  
Structural Equation ◽  
Least Squares Method ◽  
Fundamental Problem ◽  
Equation Modeling ◽  
Path Coefficient ◽  
Least Squares Regression

The most fundamental problem currently associated with structural equation modeling employing the partial least squares method is that it does not properly account for measurement error, which often leads to path coefficient estimates that asymptotically converge to values of lower magnitude than the true values. This attenuation phenomenon affects applications in the field of business data analytics; and is in fact a characteristic of composite-based models in general, where latent variables are modeled as exact linear combinations of their indicators. The underestimation is often of around 10% per path in models that meet generally accepted measurement quality assessment criteria. The authors propose a numeric solution to this problem, which they call the factor-based partial least squares regression (FPLSR) algorithm, whereby variation lost in composites is restored in proportion to measurement error and amount of attenuation. Six variations of the solution are developed based on different reliability measures, and contrasted in Monte Carlo simulations. The authors' solution is nonparametric and seems to perform generally well with small samples and severely non-normal data.

Parameter Identification of the Non-Linear Rolling Damping Based on PLS Regression Technique

2013 ◽  
Vol 779-780 ◽  
pp. 675-679 ◽  
Author(s):  
Li Ping Sun ◽  
Wei Ben Sun
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Partial Least Squares Regression ◽  
Least Squares Method ◽  
Least Squares Regression ◽  
Marine Structures ◽  
Roll Damping ◽  
Experimental Conditions ◽  
Non Linear

The rolling motion analysis of ships or other marine structures is of paramount importance. However, one of the thorniest issues in the analysis is the determination of roll damping. The main objective of this work is to apply the Partial Least-Squares regression into the Bass Energy Method and Roberts Method, which are used for the identification of non-linear roll damping parameters. When the number of sample points decreases due to limitations of the experimental conditions and other factors, the differences between the results obtained from Partial Least-Squares Regression and from traditional Least-Squares method demonstrate the applicability of the proposed method.

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