Use of a priori information in the identification of global nonlinear models-a case study using a buck converter

Author(s):  
L.A. Aguirre ◽  
P.F. Donoso-Garcia ◽  
R. Santos-Filho
2020 ◽  
Vol 12 (16) ◽  
pp. 2553
Author(s):  
Bo Zhong ◽  
Qiong Li ◽  
Jianli Chen ◽  
Zhicai Luo ◽  
Hao Zhou

We presented an improved method for estimation of regional surface mass variations from the Gravity Recovery and Climate Experiment (GRACE)-derived precise intersatellite geopotential differences using a priori constraints. An alternative analytic formula was proposed to incorporate the K-band ranging (KBR) range rate into the improved energy balance equation, and precise geopotential differences were estimated from GRACE Level-1B data based on the remove-compute-restore (RCR) technique, which avoids the long-wavelength gravity signals being absorbed by empirical parameters. To reduce the ill condition for inversion of regional mass variations from geopotential differences, a priori information from hydrological models was used to construct the constraint equations, and the optimal regularization parameters were adaptively determined based on iterative least-squares estimation. To assess our improved method, a case study of regional mass variations’ inversion was carried out over South America on 2° × 2° grids at monthly intervals from January 2005 to December 2010. The results show that regional mascon solutions inverted from geopotential differences estimated by the RCR technique using hydrological models as a priori constraints can retain more signal energy and enhance regional mass variation inversion. The spatial distributions and annual amplitudes of geopotential difference-based regional mascon solutions agree well with the official GRACE mascon solutions, although notable differences exist in spatial patterns and trends, especially in small basins. In addition, our improved method can robustly estimate the mascon solutions, which are less affected by the a priori information. The results from the case study have clearly demonstrated the feasibility and effectiveness of the proposed method.


2008 ◽  
Vol 08 (03n04) ◽  
pp. L249-L260 ◽  
Author(s):  
GIUSEPPE CABRAS ◽  
ROBERTO CARNIEL ◽  
JOACHIM WASSERMANN

Independent Component Analysis (ICA) is an emerging new technique in the blind identification of signals recorded in a variety of different fields. ICA tries to find the most statistically independent sources from an observable random vector, with the only restriction that all sources but at most one are non-Gaussian; no other a priori information on sources and mixing dynamic system are needed. The applications of these techniques to the analysis of volcanic time series are relatively few to date. In this paper we show that ICA is a suitable technique to separate a volcanic source component from ocean microseisms background noise in a seismic dataset recorded at the Mt. Merapi volcano, Indonesia. The encouraging results obtained with this methodology in the presented case study support their wider applicability in volcano seismology.


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