Cascade Backward Propagation Neural Network and Multiple Regression in the Case of Heteroscedasticity

2016 ◽  
Vol 18 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Mamman Mamuda ◽  
Saratha Sathasivam
Keyword(s):  
Neural Network ◽  
Multiple Regression ◽  
Backward Propagation

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.

scholarly journals Dynamic Modulus Prediction of a High-Modulus Asphalt Mixture

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chaohui Wang ◽  
Songyuan Tan ◽  
Qian Chen ◽  
Jiguo Han ◽  
Liang Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Multiple Regression ◽  
Dynamic Modulus ◽  
Prediction Error ◽  
Prediction Models ◽  
Asphalt Mixture ◽  
High Modulus ◽  
Output Stability ◽  
Svm Model

Dynamic modulus is a key evaluation index of the high-modulus asphalt mixture, but it is relatively difficult to test and collect its data. The purpose is to achieve the accurate prediction of the dynamic modulus of the high-modulus asphalt mixture and further optimize the design process of the high-modulus asphalt mixture. Five high-temperature performance indexes of high-modulus asphalt and its mixture were selected. The correlation between the above five indexes and the dynamic modulus of the high-modulus asphalt mixture was analyzed. On this basis, the dynamic modulus prediction models of the high-modulus asphalt mixture based on small sample data were established by multiple regression, general regression neural network (GRNN), and support vector machine (SVM) neural network. According to parameter adjustment and cross-validation, the output stability and accuracy of different prediction models were compared and evaluated. The most effective prediction model was recommended. The results show that the SVM model has more significant prediction accuracy and output stability than the multiple regression model and the GRNN model. Its prediction error was 0.98–9.71%. Compared with the other two models, the prediction error of the SVM model declined by 0.50–11.96% and 3.76–13.44%. The SVM neural network was recommended as the dynamic modulus prediction model of the high-modulus asphalt mixture.

scholarly journals Identification Accuracy of Additional Wave Resistance Through a Comparison of Multiple Regression and Artificial Neural Network Methods

2018 ◽  
Vol 1 (1) ◽  
pp. 197-204
Author(s):  
Tomasz Cepowski
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Nonlinear Models ◽  
Linear Equations ◽  
Regression Method ◽  
Wave Resistance ◽  
Identification Accuracy ◽  
Linear Regression Method ◽  
Artificial Neural

Abstract The article presents the use of multiple regression method to identify added wave resistance. Added wave resistance was expressed in the form of a four-state nominal function of: “thrust”, “zero”, “minor” and “major” resistance values. Three regression models were developed for this purpose: a regression model with linear variables, nonlinear variables and a large number of nonlinear variables. The nonlinear models were developed using the author's algorithm based on heuristic techniques. The three models were compared with a model based on an artificial neural network. This study shows that non-linear equations developed through a multiple linear regression method using the author’s algorithm are relatively accurate, and in some respects, are more effective than artificial neural networks.

scholarly journals Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network

2020 ◽  
Vol 33 (10) ◽  
pp. 1633-1641
Author(s):  
Dae-Hyun Lee ◽  
Seung-Hyun Lee ◽  
Byoung-Kwan Cho ◽  
Collins Wakholi ◽  
Young-Wook Seo ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Regression ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Carcass Weight ◽  
Body Measurements ◽  
Least Square Regression ◽  
Data Points ◽  
Hanwoo Cattle

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network.Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation.Results: The R2 values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R2 values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy.Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

Sign in / Sign up

Export Citation Format

Share Document