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» A new kernel-based approach for linear system identification
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CVPR
2005
IEEE
14 years 6 months ago
Kernel-Based Bayesian Filtering for Object Tracking
Particle filtering provides a general framework for propagating probability density functions in non-linear and non-Gaussian systems. However, the algorithm is based on a Monte Ca...
Bohyung Han, Ying Zhu, Dorin Comaniciu, Larry S. D...
CVPR
2004
IEEE
14 years 6 months ago
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
SIAMCO
2000
123views more  SIAMCO 2000»
13 years 4 months ago
Adaptive LQG Control of Input-Output Systems---A Cost-biased Approach
In this paper, we consider linear systems in input-output form and introduce a new adaptive linear quadratic Gaussian (LQG) control scheme which is shown to be self-optimizing. The...
Maria Prandini, Marco C. Campi
CDC
2008
IEEE
126views Control Systems» more  CDC 2008»
13 years 6 months ago
Subspace identification using predictor estimation via Gaussian regression
In this paper we propose a new nonparametric approach to identification of linear time invariant systems using subspace methods. The nonparametric paradigm to prediction of station...
Alessandro Chiuso, Gianluigi Pillonetto, Giuseppe ...
ICASSP
2011
IEEE
12 years 8 months ago
Identification of MISO nonlinear systems via the semiparametric approach
In this paper we examine a class of multiple-input, singleoutput (MISO) nonlinear systems of the block-oriented structure. In particular, we focus on MISO Hammerstein systems bein...
Jiaqing Lv, Miroslaw Pawlak