scholarly journals A Novel Hybrid Data-Driven Modeling Method for Missiles

Symmetry ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 30
Author(s):  
Yongxiang He ◽  
Hongwu Guo ◽  
Yang Han
Keyword(s):  
System Modeling ◽  
Nonlinear Dynamic System ◽  
Flight Test ◽  
Modeling Method ◽  
Data Driven ◽  
Simulation Experiments ◽  
Hybrid Data ◽  
Mechanism Modeling ◽  
Dynamic System Modeling ◽  
Data Driven Modeling

This paper proposes a novel hybrid data-driven modeling method for missiles. Based on actual flight test data, the missile hybrid model is established by combining neural networks and the mechanism modeling method, considering the uncertainties and nonlinear factors in missiles. This method can avoid the problems in missile mechanism modeling and traditional data-driven modeling, and can also provide a solution for nonlinear dynamic system modeling problems in offline usage scenarios. Finally, the feasibility of the proposed method and the credibility of the established model are verified by simulation experiments and statistical analysis.

scholarly journals A data-driven modeling method to analyze cardiomyocyte impedance data

2018 ◽  
Vol 93 ◽  
pp. 140
Author(s):  
Levy Batista ◽  
Thierry Bastogne ◽  
Franck Atienzar ◽  
Annie Delaunois ◽  
Jean-Pierre Valentin
Keyword(s):  
Modeling Method ◽  
Data Driven ◽  
Impedance Data ◽  
Data Driven Modeling

scholarly journals Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics

2020 ◽  
Vol 5 (4) ◽  
pp. 29
Author(s):  
Sergei Belov ◽  
Sergei Nikolaev ◽  
Ighor Uzhinsky
Keyword(s):  
Gas Turbine ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Flame Tube ◽  
Learning Techniques ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Data Driven Modeling ◽  
Engine Power

This paper presents a methodology for predictive and prescriptive analytics of a gas turbine. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnosis of its flame tube. The developed approach allows not just to analyze and predict some problems in the combustion chamber, but also to identify a particular flame tube to be repaired or replaced and plan maintenance actions in advance.

Quantification of Environmental Effects on PV Module Degradation: A Physics-Based Data-Driven Modeling Method

2018 ◽  
Vol 8 (5) ◽  
pp. 1289-1296 ◽  
Author(s):  
Arun Bala Subramaniyan ◽  
Rong Pan ◽  
Joseph Kuitche ◽  
GovindaSamy TamizhMani
Keyword(s):  
Environmental Effects ◽  
Modeling Method ◽  
Data Driven ◽  
Pv Module ◽  
Data Driven Modeling

A BIM-Based Data-Driven Modeling Method

2021 ◽  
Author(s):  
Luqi Wang ◽  
Bingke Zhao ◽  
Qizhi Ye ◽  
Anqi Feng ◽  
Weimin Feng
Keyword(s):  
Modeling Method ◽  
Data Driven ◽  
Data Driven Modeling

scholarly journals Hybrid Data-Driven and Physics-Based Modelling for Gas-Turbine Prescriptive Analytics

2020 ◽  
Author(s):  
Sergei Belov ◽  
Sergei Nikolaev ◽  
Ighor Uzhinsky
Keyword(s):  
Data Driven ◽  
Machine Learning Techniques ◽  
Engineering Systems ◽  
Learning Techniques ◽  
Prescriptive Analytics ◽  
Hybrid Data ◽  
Complex Engineering ◽  
Data Driven Modeling ◽  
Complex Engineering Systems

This paper presents a methodology for predictive and prescriptive analytics of complex engineering systems. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnostics of its flame tube.

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