Automatic Abstraction of Time-Varying System Models for Model Based Diagnosis

2005 ◽  
pp. 176-190 ◽  
Author(s):  
Pietro Torasso ◽  
Gianluca Torta
Keyword(s):  
Time Varying ◽  
System Models ◽  
Model Based ◽  
Automatic Abstraction

scholarly journals Model-based feed-forward control for time-varying systems with an example for SRF cavities

2020 ◽  
Vol 53 (2) ◽  
pp. 1331-1336
Author(s):  
Sven Pfeiffer ◽  
Annika Eichler ◽  
Holger Schlarb
Keyword(s):  
Time Varying ◽  
Feed Forward ◽  
Feed Forward Control ◽  
Model Based ◽  
Time Varying Systems ◽  
Srf Cavities

A survey on model-based fault diagnosis for linear discrete time-varying systems

2018 ◽  
Vol 306 ◽  
pp. 51-60 ◽  
Author(s):  
Maiying Zhong ◽  
Ting Xue ◽  
Steven X. Ding
Keyword(s):  
Fault Diagnosis ◽  
Discrete Time ◽  
Time Varying ◽  
Model Based ◽  
Time Varying Systems

Oil price risk evaluation using a novel hybrid model based on time-varying long memory

2019 ◽  
Vol 81 ◽  
pp. 70-78 ◽  
Author(s):  
Lu-Tao Zhao ◽  
Kun Liu ◽  
Xin-Lei Duan ◽  
Ming-Fang Li
Keyword(s):  
Hybrid Model ◽  
Long Memory ◽  
Risk Evaluation ◽  
Oil Price ◽  
Price Risk ◽  
Time Varying ◽  
Model Based ◽  
Oil Price Risk

scholarly journals A Novel Linear Time-Varying GM(1,N) Model for Forecasting Haze: A Case Study of Beijing, China

2019 ◽  
Vol 11 (14) ◽  
pp. 3832 ◽  
Author(s):  
Pingping Xiong ◽  
Jia Shi ◽  
Lingling Pei ◽  
Song Ding
Keyword(s):  
Linear Time ◽  
Dynamic Change ◽  
Time Varying ◽  
The Novel ◽  
Model Based ◽  
Number Sequences ◽  
The Government ◽  
Grey Number ◽  
Beijing China

Haze is the greatest challenge facing China’s sustainable development, and it seriously affects China’s economy, society, ecology and human health. Based on the uncertainty and suddenness of haze, this paper proposes a novel linear time-varying grey model (GM)(1,N) based on interval grey number sequences. Because the original GM(1,N) model based on interval grey number sequences has constant parameters, it neglects the dynamic change characteristics of parameters over time. Therefore, this novel linear time-varying GM(1,N) model, based on interval grey number sequences, is established on the basis of the original GM(1,N) model by introducing a linear time polynomial. To verify the validity and practicability of this model, this paper selects the data of PM10, SO2 and NO2 concentrations in Beijing, China, from 2008 to 2018, to establish a linear time-varying GM(1,3) model based on interval grey number sequences, and the prediction results are compared with the original GM(1,3) model. The result indicates that the prediction effect of the novel model is better than that of the original model. Finally, this model is applied to forecast PM10 concentration for 2019 to 2021 in Beijing, and the forecast is made to provide a reference for the government to carry out haze control.

scholarly journals An automatic performance model-based scheduling tool for coupled climate system models

2019 ◽  
Vol 132 ◽  
pp. 204-216 ◽  
Author(s):  
Nan Ding ◽  
Wei Xue ◽  
Zhenya Song ◽  
Haohuan Fu ◽  
Shiming Xu ◽  
...  
Keyword(s):  
Performance Model ◽  
Climate System ◽  
System Models ◽  
Model Based ◽  
Scheduling Tool ◽  
Climate System Models
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