Results 171 to 180 of about 3,247,690 (230)
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Probing inputs for subspace identification
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002There is experimental evidence that the standard subspace methods (e.g. the N4SID method) perform poorly in certain conditions where the past signals (past inputs and past outputs) and future input spaces are nearly parallel. Based on an elementary numerical conditioning analysis, the paper describes a class of (system-dependent) input signals (called ...
CHIUSO, ALESSANDRO, PICCI, GIORGIO
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RF Fingerprints Prediction for Cellular Network Positioning: A Subspace Identification Approach
IEEE Transactions on Mobile Computing, 2020Cellular network positioning is a mandatory requirement for localizing emergency callers, such as E911 in North America. Although smartphones are normally equipped with GPS modules, there are still a large number of users with cell phones only as basic ...
Xiaohua Tian +3 more
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Nonlinear Subspace Model Identification
IFAC Proceedings Volumes, 2004Abstract Canonical variates state space (CVSS) modeling is a popular subspace linear model identification technique. A nonlinear extension of CVSS modeling approach was proposed (DeCicco and Cinar, 2000) . The modeling procedure consists of two steps: development of a multivariable nonlinear model for a set of latent variables and the linking of the ...
Ali Cinar, Jeffrey DeCicco
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Nuclear norm subspace identification of continuous time state–space models with missing outputs
Control Engineering Practice, 2020Subspace identification methods using Generalized Poisson Moment Functionals (GPMF) have been proposed previously to tackle the problem of derivative estimation in continuous time (CT) systems.
S. K. Varanasi, P. Jampana
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IEEE Transactions on Geoscience and Remote Sensing
In hyperspectral remote sensing (HRS), signal subspace identification (SSID) is a critical step in many widely renowned HRS algorithms, while the accuracy of the subspace identification relies on the complete information of the data pixels.
Chia-Hsiang Lin, Si-Sheng Young
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In hyperspectral remote sensing (HRS), signal subspace identification (SSID) is a critical step in many widely renowned HRS algorithms, while the accuracy of the subspace identification relies on the complete information of the data pixels.
Chia-Hsiang Lin, Si-Sheng Young
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On-line subspace identification
2001 European Control Conference (ECC), 2001In this paper a recursive technique, based on the subspace state space identification methods, is presented for identification of time-varying systems. The main idea was to develop an iterative algorithm with most of the advantages of this kind of methods in order to deal with real-time applications and minimize the computational burden.
Catarina J. M. Delgado +2 more
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System Identification Based on Invariant Subspace
IEEE Transactions on Automatic Control, 2022This article proposes a novel system identification method based on the notion of invariant subspace. It is shown that when the system input and output asymptotically converge onto an invariant subspace, a new form of regression can be obtained.
Chao Huang +3 more
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Subspace identification methods and fMRI analysis
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008The main goal of this paper is to propose application of modern multidimensional systems identification algorithms of the subspace identification theory in the context of fMRI data analysis. The methods originated in 1990s in the field of process control and identification and yield robust linear model parameter estimates for systems with many inputs ...
Jana, Tauchmanova, Martin, Hromcik
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Adaptive radar detection: A subspace identification approach
2010 2nd International Workshop on Cognitive Information Processing, 2010We address adaptive detection of Swerling 2 pulse trains by an array of antennas. The disturbance is modeled in terms of a state space model and the ideas of subspace identification are used to come up with a GLRT-based detector. Such detector is compared by Monte Carlo simulation with a Kelly's detector derived assuming that returns are temporally ...
BANDIERA, Francesco +3 more
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Hybrid Modeling Approach Integrating First-Principles Models with Subspace Identification
Industrial & Engineering Chemistry Research, 2019This paper addresses the problem of synergizing first-principles models with data-driven models. This is achieved by building a hybrid model where the subspace model identification algorithm is used to create a model for the residuals (mismatch in the ...
Debanjan Ghosh +4 more
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