11,180 research outputs found
ارزيابي اثربخشي دوره هاي آموزش ضمن خدمت کارکنان دانشگاه علوم پزشکی قزوین بر اساس مدل ارزشيابي كرك پاتريك
We present a system identification method for problems with partially missing inputs and outputs. The method is based on a subspace formulation and uses the nuclear norm heuristic for structured low-rank matrix approximation, with the missing input and output values as the optimization variables. We also present a fast implementation of the alternating direction method of multipliers (ADMM) to solve regularized or non-regularized nuclear norm optimization problems with Hankel structure. This makes it possible to solve quite large system identification problems. Experimental results show that the nuclear norm optimization approach to subspace identification is comparable to the standard subspace methods when no inputs and outputs are missing, and that the performance degrades gracefully as the percentage of missing inputs and outputs increases.Funding Agencies|National Science Foundation|1128817|</p
Robust Subspace System Identification via Weighted Nuclear Norm Optimization
Subspace identification is a classical and very well studied problem in
system identification. The problem was recently posed as a convex optimization
problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this
framework to handle outliers. The proposed framework takes the form of a convex
optimization problem with an objective that trades off fit, rank and sparsity.
As in robust PCA, it can be problematic to find a suitable regularization
parameter. We show how the space in which a suitable parameter should be sought
can be limited to a bounded open set of the two dimensional parameter space. In
practice, this is very useful since it restricts the parameter space that is
needed to be surveyed.Comment: Submitted to the IFAC World Congress 201
Maximum Entropy Vector Kernels for MIMO system identification
Recent contributions have framed linear system identification as a
nonparametric regularized inverse problem. Relying on -type
regularization which accounts for the stability and smoothness of the impulse
response to be estimated, these approaches have been shown to be competitive
w.r.t classical parametric methods. In this paper, adopting Maximum Entropy
arguments, we derive a new penalty deriving from a vector-valued
kernel; to do so we exploit the structure of the Hankel matrix, thus
controlling at the same time complexity, measured by the McMillan degree,
stability and smoothness of the identified models. As a special case we recover
the nuclear norm penalty on the squared block Hankel matrix. In contrast with
previous literature on reweighted nuclear norm penalties, our kernel is
described by a small number of hyper-parameters, which are iteratively updated
through marginal likelihood maximization; constraining the structure of the
kernel acts as a (hyper)regularizer which helps controlling the effective
degrees of freedom of our estimator. To optimize the marginal likelihood we
adapt a Scaled Gradient Projection (SGP) algorithm which is proved to be
significantly computationally cheaper than other first and second order
off-the-shelf optimization methods. The paper also contains an extensive
comparison with many state-of-the-art methods on several Monte-Carlo studies,
which confirms the effectiveness of our procedure
Kernel-based system identification from noisy and incomplete input-output data
In this contribution, we propose a kernel-based method for the identification
of linear systems from noisy and incomplete input-output datasets. We model the
impulse response of the system as a Gaussian process whose covariance matrix is
given by the recently introduced stable spline kernel. We adopt an empirical
Bayes approach to estimate the posterior distribution of the impulse response
given the data. The noiseless and missing data samples, together with the
kernel hyperparameters, are estimated maximizing the joint marginal likelihood
of the input and output measurements. To compute the marginal-likelihood
maximizer, we build a solution scheme based on the Expectation-Maximization
method. Simulations on a benchmark dataset show the effectiveness of the
method.Comment: 16 pages, submitted to IEEE Conference on Decision and Control 201
Search for the Standard Model Higgs Boson with the OPAL Detector at LEP
This paper summarises the search for the Standard Model Higgs boson in e+e-
collisions at centre-of-mass energies up to 209 GeV performed by the OPAL
Collaboration at LEP. The consistency of the data with the background
hypothesis and various Higgs boson mass hypotheses is examined. No indication
of a signal is found in the data and a lower bound of 112.7GeV/C^2 is obtained
on the mass of the Standard Model Higgs boson at the 95% CL.Comment: 51 pages, 21 figure
Proximal algorithms for nuclear norm system identification
In this thesis, we present four proximal algorithms for the solution of a nuclear norm optimization problem. Respect to other papers, we solve a more generic version of this problem. Another contribution is related in particular to the model order selection, in fact, we propose to use the parsimony principle. Experimentally with this method, we obtain a better fit with a lower model order
Identification from data with periodically missing output samples
The identification problem in case of data with missing values is challenging and currently not fully understood. For example, there are
no general nonconservative identifiability results, nor provably correct data efficient methods. In this paper, we consider a special case
of periodically missing output samples, where all but one output sample per period may be missing. The novel idea is to use a lifting
operation that converts the original problem with missing data into an equivalent standard identification problem. The key step is the
inverse transformation from the lifted to the original system, which requires computation of a matrix root. The well-posedness of the
inverse transformation depends on the eigenvalues of the system. Under an assumption on the eigenvalues, which is not verifiable from
the data, and a persistency of excitation-type assumption on the data, the method based on lifting recovers the data-generating system.
(Preprint submitted to Automatica)European Research Council (ERC)/European Union’s Horizon 2020 research and innovation programme/948679/E
Data-based system representations from irregularly measured data
Non-parametric representations of dynamical systems based on the image of a
Hankel matrix of data are extensively used for data-driven control. However, if
samples of data are missing, obtaining such representations becomes a difficult
task. By exploiting the kernel structure of Hankel matrices of irregularly
measured data generated by a linear time-invariant system, we provide
computational methods for which any complete finite-length behavior of the
system can be obtained. For the special case of periodically missing outputs,
we provide conditions on the input such that the former result is guaranteed.
We illustrate with an example how the resulting representation provides a more
computationally efficient method for low-rank matrix completion when compared
to an alternative method.Comment: submitted to IEEE Transactions on Automatic Contro
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