Robust Estimation for Partial Functional Linear Regression Model Based on Modal Regression

2020 ◽  
Vol 33 (2) ◽  
pp. 527-544
Author(s):  
Ping Yu ◽  
Zhongyi Zhu ◽  
Jianhong Shi ◽  
Xikai Ai
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Robust Estimation ◽  
Linear Regression Model ◽  
Model Based ◽  
Modal Regression ◽  
Functional Linear Regression

scholarly journals A Novel Output Prediction Method in Production Management Based on Parameter Evaluation Using DHNN

2013 ◽  
Vol 2013 ◽  
pp. 1-10
Author(s):  
Jiantao Chang ◽  
Yuanying Qiu ◽  
Xianguang Kong
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Production Management ◽  
Prediction Method ◽  
Multiple Linear Regression Model ◽  
Model Based ◽  
Parameter Evaluation ◽  
Improved Model

Output prediction is one of the difficult issues in production management. To overcome this difficulty, a dynamic-improved multiple linear regression model based on parameter evaluation using discrete Hopfield neural networks (DHNN) is presented. First, a traditional multiple linear regression model is established; this model takes the factors in production lifecycle (not only one phase of the production) into account, such as manufacturing resources, manufacturing process, and product rejection rate, so it makes the output prediction be more accurate. Then a static-improved model is built using the backstepping method. Finally, we obtain the dynamic-improved model based on parameter evaluation using DHNN. These three models are applied to an aviation manufacturing enterprise based on the actual data, and the results of the output prediction show that the models have practical value.

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