scholarly journals The research of regression model in machine learning field

2018 ◽  
Vol 176 ◽  
pp. 01033 ◽  
Author(s):  
Shen Rong ◽  
Zhang Bao-wen
Keyword(s):  
Regression Analysis ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Reference Value ◽  
The Other ◽  
Analysis Model ◽  
Model Based ◽  
Set Up ◽  
Library Function

The paper herein will analyze the sale of iced products affected by variation of temperature. Firstly, we will collect the data of the forecast temperature last year and the sale of iced products and then conduct data compilation and cleansing. Finally, we will set up the mathematical regression analysis model based on the cleansed data by means of data mining theory. Regression analysis refers to the method of studying the relationship between independent variable and dependent variable. Linear regression model that corresponds to the practical situation is proposed in the paper, which is to set up simple linear regression model based on practical problem and then to implement the following with the help of the latest and most popular Python3.6. Python3.6 boasts the features of pure object-oriented, platform independence and concise and elegant language. So we will call the corresponding library function to predict the sale of iced products according to the variation of temperature, which will provide the foundation for the company to adjust its production each month, or even each week and each day. As a result, the situation of overproduction can be avoided. Moreover, the other situation as the profit will be affected by the lack of production since the rise of temperature will also be avoided. So the regression model also has reference value for the other fields of marketing.

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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