Unbiased estimation of parameters of linear continuous model based on bias correction with input/output measurement noises

2011 ◽  
Author(s):  
He Shang-hong ◽  
Shi Fang ◽  
Zhao Tian-lin
Keyword(s):  
Bias Correction ◽  
Continuous Model ◽  
Unbiased Estimation ◽  
Input Output ◽  
Estimation Of Parameters ◽  
Output Measurement ◽  
Model Based ◽  
Measurement Noises

scholarly journals Carbon emissions and driving forces of China’s power sector: Input-output model based on the disaggregated power sector

2020 ◽  
Vol 268 ◽  
pp. 121925 ◽  
Author(s):  
Fang Luo ◽  
Yi Guo ◽  
Mingtao Yao ◽  
Wenqiu Cai ◽  
Meng Wang ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Driving Forces ◽  
Input Output ◽  
Power Sector ◽  
Model Based

scholarly journals Quantification of dynamic cerebral autoregulation and CO2 dynamic vasomotor reactivity impairment in essential hypertension

2020 ◽  
Vol 128 (2) ◽  
pp. 397-409
Author(s):  
Vasilis Z. Marmarelis ◽  
Dae C. Shin ◽  
Mareike Oesterreich ◽  
Martin Mueller
Keyword(s):  
Essential Hypertension ◽  
Cerebral Autoregulation ◽  
Input Output ◽  
Physiological Mechanisms ◽  
Control Subjects ◽  
Vasomotor Reactivity ◽  
Model Based ◽  
Arterial Blood ◽  
Transfer Function Analysis ◽  
Dynamic Cerebral Autoregulation

The study of dynamic cerebral autoregulation (DCA) in essential hypertension has received considerable attention because of its clinical importance. Several studies have examined the dynamic relationship between spontaneous beat-to-beat arterial blood pressure data and contemporaneous cerebral blood flow velocity measurements (obtained via transcranial Doppler at the middle cerebral arteries) in the form of a linear input-output model using transfer function analysis. This analysis is more reliable when the contemporaneous effects of changes in blood CO2 tension are also taken into account, because of the significant effects of CO2 dynamic vasomotor reactivity (DVR) upon cerebral flow. In this article, we extract such input-output predictive models from spontaneous time series hemodynamic data of 24 patients with essential hypertension and 20 normotensive control subjects under resting conditions, using the novel methodology of principal dynamic modes (PDMs) that achieves improved estimation accuracy over previous methods for relatively short and noisy data. The obtained data-based models are subsequently used to compute indexes and markers that quantify DCA and DVR in each subject or patient and therefore can be used to assess the effects of essential hypertension. These model-based DCA and DVR indexes were properly defined to capture the observed effects of DCA and VR and found to be significantly different ( P < 0.05) in the hypertensive patients. We also found significant differences between patients and control subjects in the relative contribution of three PDMs to the model output prediction, a finding that offers the prospect of identifying the physiological mechanisms affected by essential hypertension when the PDMs are interpreted in terms of specific physiological mechanisms. NEW & NOTEWORTHY This article presents novel model-based methodology for obtaining diagnostic indexes of dynamic cerebral autoregulation and dynamic vasomotor reactivity in hypertension.

scholarly journals Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

2016 ◽  
Vol 16 (06) ◽  
pp. 1550016 ◽  
Author(s):  
Mohsen Askari ◽  
Jianchun Li ◽  
Bijan Samali
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Structural Parameters ◽  
Measurement Data ◽  
Linear Structure ◽  
Structural Identification ◽  
Input Output ◽  
Output Measurement ◽  
Highly Nonlinear

System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input–output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input–output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.

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