Monitoring parameter change in linear regression model based on the efficient score vector

2019 ◽  
Vol 527 ◽  
pp. 121135
Author(s):  
Wenzhi Zhao ◽  
Yixin Xue ◽  
Xin Liu
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Parameter Change ◽  
Efficient Score ◽  
Model Based ◽  
Score Vector ◽  
Monitoring Parameter

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.

scholarly journals CORAL: the prediction of biodegradation of organic compounds with optimal SMILES-based descriptors

2012 ◽  
Vol 10 (4) ◽  
pp. 1042-1048 ◽  
Author(s):  
Andrey Toropov ◽  
Alla Toropova ◽  
Anna Lombardo ◽  
Alessandra Roncaglioni ◽  
Nicoletta Brita ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Organic Compounds ◽  
Linear Regression Model ◽  
Correlation Coefficients ◽  
Coral Software ◽  
Quantitative Structure ◽  
Model Based ◽  
Test Sets ◽  
Sensitivity Specificity

AbstractCORAL software (http:/www.insilico.eu/coral) has been used to build up quantitative structure-biodegradation relationships (QSPR). The normalized degradation percentage has been used as the measure of biodegradation (for diverse organic compounds, n=445). Six random splits into sub-training, calibration, and test sets were examined. For each split the QSPR one-variable linear regression model based on the SMILES-based optimal descriptors has been built up. The average values of numbers of compounds and the correlation coefficients (r2) between experimental and calculated biodegradability values of these six models for the test sets are n=88.2±11.7 and r2=0.728±0.05. These six models were further tested against a set of chemicals (n=285) for which only categorical values (biodegradable or not) were available. Thus we also evaluated the use of the model as a classifier. The average values of the sensitivity, specificity, and accuracy were 0.811±0.019, 0.795±0.024, and 0.803±0.008, respectively.

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