scholarly journals Novel Ensemble Modeling Method for Enhancing Subset Diversity Using Clustering Indicator Vector Based on Stacked Autoencoder

2019 ◽  
Vol 121 (1) ◽  
pp. 123-144
Author(s):  
Yanzhen Wang ◽  
Xuefeng Yan
Keyword(s):  
Modeling Method ◽  
Ensemble Modeling ◽  
Stacked Autoencoder ◽  
Subset Diversity

scholarly journals Prediction of the Solubility of CO2 in Imidazolium Ionic Liquids Based on Selective Ensemble Modeling Method

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1369
Author(s):  
Luyue Xia ◽  
Shanshan Liu ◽  
Haitian Pan
Keyword(s):  
Ionic Liquids ◽  
Co2 Capture ◽  
Entropy Method ◽  
Modeling Method ◽  
Ensemble Model ◽  
Ensemble Modeling ◽  
Co2 Solubility ◽  
Fully Integrated ◽  
Imidazolium Ionic Liquids ◽  
Selective Ensemble

Solubility data is one of the essential basic data for CO2 capture by ionic liquids. A selective ensemble modeling method, proposed to overcome the shortcomings of current methods, was developed and applied to the prediction of the solubility of CO2 in imidazolium ionic liquids. Firstly, multiple different sub–models were established based on the diversities of data, structural, and parameter design philosophy. Secondly, the fuzzy C–means algorithm was used to cluster the sub–models, and the collinearity detection method was adopted to eliminate the sub–models with high collinearity. Finally, the information entropy method integrated the sub–models into the selective ensemble model. The validation of the CO2 solubility predictions against experimental data showed that the proposed ensemble model had better performance than its previous alternative, because more effective information was extracted from different angles, and the diversity and accuracy among the sub–models were fully integrated. This work not only provided an effective modeling method for the prediction of the solubility of CO2 in ionic liquids, but also provided an effective method for the discrimination of ionic liquids for CO2 capture.

A new ensemble modeling method for multivariate calibration of near infrared spectra

2021 ◽  
Vol 13 (11) ◽  
pp. 1374-1380
Author(s):  
Kaiyi Wang ◽  
Xihui Bian ◽  
Xiaoyao Tan ◽  
Haitao Wang ◽  
Yankun Li
Keyword(s):  
Infrared Spectra ◽  
Near Infrared ◽  
Multivariate Calibration ◽  
Modeling Method ◽  
Ensemble Modeling ◽  
Near Infrared Spectra

A new ensemble modeling method based on PLS, MC sampling and LASSO was proposed for multivariate calibration of near infrared spectra.

scholarly journals An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction

2019 ◽  
Vol 9 (18) ◽  
pp. 3689 ◽  
Author(s):  
Siyu Ye ◽  
Yi Zhang ◽  
Wen Yao ◽  
Quan Chen ◽  
Xiaoqian Chen
Keyword(s):  
Prediction Accuracy ◽  
Traffic Volume ◽  
Engineering Problem ◽  
Modeling Method ◽  
Distribution Data ◽  
Communication Link ◽  
Ensemble Modeling ◽  
Business Services ◽  
Table Lookup ◽  
Data Table

The satellite constellation network is a powerful tool to provide ground traffic business services for continuous global coverage. For the resource-limited satellite network, it is necessary to predict satellite coverage traffic volume (SCTV) in advance to properly allocate onboard resources for better task fulfillment. Traditionally, a global SCTV distribution data table is first statistically constructed on the ground according to historical data and uploaded to the satellite. Then SCTV is predicted onboard by a data table lookup. However, the cost of the large data transmission and storage is expensive and prohibitive for satellites. To solve these problems, this paper proposes to distill the data into a surrogate model to be uploaded to the satellite, which can both save the valuable communication link resource and improve the SCTV prediction accuracy compared to the table lookup. An effective surrogate ensemble modeling method is proposed in this paper for better prediction. First, according to prior geographical knowledge of the SCTV distribution, the global earth surface domain is split into multiple sub-domains. Second, on each sub-domain, multiple candidate surrogates are built. To fully exploit these surrogates and combine them into a more accurate ensemble, a partial weighted aggregation method (PWTA) is developed. For each sub-domain, PWTA adaptively selects the candidate surrogates with higher accuracy as the contributing models, based on which the ultimate ensemble is constructed for each sub-domain SCTV prediction. The proposed method is demonstrated and testified with an air traffic SCTV engineering problem. The results demonstrate the effectiveness of PWTA regarding good local and global prediction accuracy and modeling robustness.

Human Behavior Modeling Method Based on the Causality Between the Situation and the Behavior

2011 ◽  
Vol 131 (3) ◽  
pp. 635-643 ◽  
Author(s):  
Kohjiro Hashimoto ◽  
Kae Doki ◽  
Shinji Doki ◽  
Shigeru Okuma ◽  
Akihiro Torii
Keyword(s):  
Human Behavior ◽  
Modeling Method ◽  
Behavior Modeling ◽  
Human Behavior Modeling
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