scholarly journals An intelligent approach of controlled variable selection for constrained process self-optimizing control

2022 ◽  
Vol 10 (1) ◽  
pp. 65-72
Author(s):  
Hongxin Su ◽  
Chenchen Zhou ◽  
Yi Cao ◽  
Shuang-Hua Yang ◽  
Zuzhen Ji
Keyword(s):  
Variable Selection ◽  
Selection For ◽  
Optimizing Control ◽  
Intelligent Approach

scholarly journals Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

2019 ◽  
Vol 7 (1) ◽  
pp. 1597956
Author(s):  
Carlos Valencia ◽  
Sergio Cabrales ◽  
Laura Garcia ◽  
Juan Ramirez ◽  
Diego Calderona ◽  
...  
Keyword(s):  
Variable Selection ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Bankruptcy Prediction ◽  
Selection For

scholarly journals Flexible variable selection for recovering sparsity in nonadditive nonparametric models

Biometrics ◽  
2016 ◽  
Vol 72 (4) ◽  
pp. 1155-1163 ◽  
Author(s):  
Zaili Fang ◽  
Inyoung Kim ◽  
Patrick Schaumont
Keyword(s):  
Variable Selection ◽  
Nonparametric Models ◽  
Selection For

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

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