Predication of GRT Fiber-Rubberized Haydite Concrete Bend Strength Based on Multiple Regression Analysis and BP Neural Network

2012 ◽  
Vol 174-177 ◽  
pp. 1100-1106
Author(s):  
Bing Hua Xia ◽  
Yuan Cai Liu ◽  
Wei Wei Sun
Keyword(s):  
Neural Network ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Test Data ◽  
Bp Neural Network ◽  
Hydration Reaction ◽  
Intensity Level ◽  
Regression Equations ◽  
Bend Strength

Experiment with intensity level for the LC30 ceramsite concrete as the research object, changing the content of cement, GRT fiber, rubber powder by the orthogonal test to configure GRT fiber—rubberized haydite concrete samples, maintenance samples 7d and 28d in standard conditions and respectively testing their bend strength. Through the analysis of the test data, using multiple regression analysis established the GRT fiber—rubberized haydite concrete 7d and 28d bend strength regression formulas.By means of BP neural network theory combine MATLAB programme established GRT fiber—rubberized haydite concrete 7d and 28d bend strength neural network model.Finally using 3 groups new test data to compare the value of multiple regression equations and BP neural network’s predicted value.The results indicate that the multiple regression equations of 28d’s and 28d’s BP neural network model are availabled.But because of the water and cement which in the GRT fiber—rubberized haydite concrete can not hydration reaction sufficiently during the 7d’s,so the multiple regression equations of 7d’s is unavailabled.

Predicition of GRT Fiber-Rubberized Haydite Concrete Compressive Strength Based on Multiple Regreeion Analysis and BP Neural Network

2012 ◽  
Vol 472-475 ◽  
pp. 60-65
Author(s):  
Bing Hua Xia ◽  
Yuan Cai Liu ◽  
De Bin Zhu
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Multiple Regression ◽  
Test Data ◽  
Bp Neural Network ◽  
Intensity Level ◽  
Regression Equations ◽  
Strength Based ◽  
Neural Network Theory ◽  
Bp Neural Network Model

Experiment with intensity level for the LC30 ceramsite concrete as the research object, changing the content of cement, GRT fiber, rubber powder by the orthogonal test to configure GRT fiber—rubberized haydite concrete samples, maintenance samples 7d and 28d in standard conditions and respectively testing their standard compressive strength. Through the analysis of the test data, using multiple regression analysis established the GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength regression formulas.By means of BP neural network theory combine MATLAB programme established GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength neural network model.Finally using 3 groups new test data to compare the value of multiple regression equations and BP neural network’s predicted value.The results indicate that the multiple regression equations and BP neural network model are availabled.

Consumers' perception-oriented product form design using multiple regression analysis and backpropagation neural network

2015 ◽  
Vol 30 (1) ◽  
pp. 64-77 ◽  
Author(s):  
Hung-Yuan Chen ◽  
Hua-Cheng Chang
Keyword(s):  
Neural Network ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Neural Network Model ◽  
Integrated Design ◽  
Consumer Response ◽  
Product Form ◽  
Backpropagation Neural Network ◽  
Integrated Design Approach

AbstractConsumers' psychological perceptions of a product are significantly influenced by its appearance aesthetics, and thus product form plays an essential role in determining the commercial success of a product. The evolution of a product's form during the design process is typically governed by the designer's individual preferences and creative instincts. As a consequence, there is a risk that the product form may fail to satisfy the consumers' expectations or may induce an unanticipated consumer response. This study commences developing an integrated design approach based on the numerical definition of product form. A series of evaluation trials are then performed to establish the correlation between the product form features and the consumers' perceptions of the product image. The results of the evaluation trials are used to construct three different types of mathematical model (a multiple regression analysis model, a backpropagation neural network model, and a multiple regression analysis with a backpropagation neural network model) to predict the likely consumer response to any arbitrary product form. The feasibility of an integrated design approach is demonstrated using a three-dimensional knife form. Although this study takes an example for illustration and verification purposes, the methodology proposed in the present study is equally applicable to any form of consumer product.

Determination of Meat Quality by Image Processing and Neural Network Techniques

2000 ◽  
Vol 12 (4) ◽  
pp. 474-479
Author(s):  
Kazuhiko Shiranita ◽  
◽  
Kenichiro Hayashi ◽  
Akifumi Otsubo
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Meat Quality ◽  
Distribution Density ◽  
Marbling Score ◽  
Quality Grading

We study the implementation of a meat-quality grading system, using the concept of the marbling score, and image processing, neural network techniques and multiple regression analysis. The marbling score is a measure of the distribution density of fat in the rib-eye region. We identify five features used for grading meat images. For the evaluation of the five features, we propose a method of image binarization using a three-layer neural network developed based on inputs given by a professional grader and a system of meat-quality grading based on the evaluation of three of five features with multiple regression analysis. Experimental results show that the system is effective.

scholarly journals Cephalometric Floating Norms as a Guide toward a Harmonious Individual Craniofacial Pattern among Filipinos

2009 ◽  
Vol 79 (6) ◽  
pp. 1162-1168 ◽  
Author(s):  
Marian Almyra Sevilla-Naranjilla ◽  
Ingrid Rudzki-Janson
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Standard Error ◽  
Pearson Correlation ◽  
Linear Regression Analysis ◽  
Regression Equations ◽  
Correlated Variables ◽  
The Individual

Abstract Objective: To construct a harmony box based on correlated cephalometric variables, which may serve as a valuable diagnostic tool in orthodontic treatment planning, by analyzing the harmonious relationships of existing individual craniofacial patterns among Filipinos. Materials and Methods: Eighty-one subjects, 37 females and 44 males, were selected from the student population of a University according to established inclusion criteria. Five cephalometric angular measurements were obtained and digitized. Pearson correlation coefficients described the high association among the five variables. The bivariate linear regression analysis was used to construct a harmony box, which contained the cephalometric floating norms of the five correlated variables. Multiple regression analysis and the standard error of the estimate were calculated to construct the harmony schema, which describes the individual craniofacial pattern. Results: Correlations between the five variables were significant at .001 and .05 levels. Linear regression equations with corresponding r2 and standard error of the estimate (SE) were illustrated as the harmony box. The multiple correlation coefficient R, the adjusted R2, and the SE when one of the five measured variables was predicted from the remaining four by means of a multiple regression analysis were displayed as the harmony schema. Conclusion: The cephalometric floating norms describing the individual craniofacial pattern among Filipinos were established based on five correlated variables in the form of a harmony box.

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