190,268 research outputs found
Identification of stable models via nonparametric prediction error methods
A new Bayesian approach to linear system identification has been proposed in
a series of recent papers. The main idea is to frame linear system
identification as predictor estimation in an infinite dimensional space, with
the aid of regularization/Bayesian techniques. This approach guarantees the
identification of stable predictors based on the prediction error minimization.
Unluckily, the stability of the predictors does not guarantee the stability of
the impulse response of the system. In this paper we propose and compare
various techniques to address this issue. Simulations results comparing these
techniques will be provided.Comment: number of pages = 6, number of figures =
Outlier robust system identification: a Bayesian kernel-based approach
In this paper, we propose an outlier-robust regularized kernel-based method
for linear system identification. The unknown impulse response is modeled as a
zero-mean Gaussian process whose covariance (kernel) is given by the recently
proposed stable spline kernel, which encodes information on regularity and
exponential stability. To build robustness to outliers, we model the
measurement noise as realizations of independent Laplacian random variables.
The identification problem is cast in a Bayesian framework, and solved by a new
Markov Chain Monte Carlo (MCMC) scheme. In particular, exploiting the
representation of the Laplacian random variables as scale mixtures of
Gaussians, we design a Gibbs sampler which quickly converges to the target
distribution. Numerical simulations show a substantial improvement in the
accuracy of the estimates over state-of-the-art kernel-based methods.Comment: 5 figure
Bayesian kernel-based system identification with quantized output data
In this paper we introduce a novel method for linear system identification
with quantized output data. We model the impulse response as a zero-mean
Gaussian process whose covariance (kernel) is given by the recently proposed
stable spline kernel, which encodes information on regularity and exponential
stability. This serves as a starting point to cast our system identification
problem into a Bayesian framework. We employ Markov Chain Monte Carlo (MCMC)
methods to provide an estimate of the system. In particular, we show how to
design a Gibbs sampler which quickly converges to the target distribution.
Numerical simulations show a substantial improvement in the accuracy of the
estimates over state-of-the-art kernel-based methods when employed in
identification of systems with quantized data.Comment: Submitted to IFAC SysId 201
People tracking and re-identification by face recognition for RGB-D camera networks
This paper describes a face recognition-based people tracking and re-identification system for RGB-D camera networks. The system tracks people and learns their faces online to keep track of their identities even if they move out from the camera's field of view once. For robust people re-identification, the system exploits the combination of a deep neural network- based face representation and a Bayesian inference-based face classification method. The system also provides a predefined people identification capability: it associates the online learned faces with predefined people face images and names to know the people's whereabouts, thus, allowing a rich human-system interaction. Through experiments, we validate the re-identification and the predefined people identification capabilities of the system and show an example of the integration of the system with a mobile robot. The overall system is built as a Robot Operating System (ROS) module. As a result, it simplifies the integration with the many existing robotic systems and algorithms which use such middleware. The code of this work has been released as open-source in order to provide a baseline for the future publications in this field
A new kernel-based approach to system identification with quantized output data
In this paper we introduce a novel method for linear system identification
with quantized output data. We model the impulse response as a zero-mean
Gaussian process whose covariance (kernel) is given by the recently proposed
stable spline kernel, which encodes information on regularity and exponential
stability. This serves as a starting point to cast our system identification
problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods
to provide an estimate of the system. In particular, we design two methods
based on the so-called Gibbs sampler that allow also to estimate the kernel
hyperparameters by marginal likelihood maximization via the
expectation-maximization method. Numerical simulations show the effectiveness
of the proposed scheme, as compared to the state-of-the-art kernel-based
methods when these are employed in system identification with quantized data.Comment: 10 pages, 4 figure
Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty
In this paper, the authors outline the general principles behind an approach to Bayesian system identification and highlight the benefits of adopting a Bayesian framework when attempting to identify models of nonlinear dynamical systems in the presence of uncertainty. It is then described how, through a summary of some key algorithms, many of the potential difficulties associated with a Bayesian approach can be overcome through the use of Markov chain Monte Carlo (MCMC) methods. The paper concludes with a case study, where an MCMC algorithm is used to facilitate the Bayesian system identification of a nonlinear dynamical system from experimentally observed acceleration time histories
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes
We introduce GP-FNARX: a new model for nonlinear system identification based
on a nonlinear autoregressive exogenous model (NARX) with filtered regressors
(F) where the nonlinear regression problem is tackled using sparse Gaussian
processes (GP). We integrate data pre-processing with system identification
into a fully automated procedure that goes from raw data to an identified
model. Both pre-processing parameters and GP hyper-parameters are tuned by
maximizing the marginal likelihood of the probabilistic model. We obtain a
Bayesian model of the system's dynamics which is able to report its uncertainty
in regions where the data is scarce. The automated approach, the modeling of
uncertainty and its relatively low computational cost make of GP-FNARX a good
candidate for applications in robotics and adaptive control.Comment: Proceedings of the 52th IEEE International Conference on Decision and
Control (CDC), Firenze, Italy, December 201
Classical vs. Bayesian methods for linear system identification: point estimators and confidence sets
This paper compares classical parametric methods with recently developed
Bayesian methods for system identification. A Full Bayes solution is considered
together with one of the standard approximations based on the Empirical Bayes
paradigm. Results regarding point estimators for the impulse response as well
as for confidence regions are reported.Comment: number of pages = 8, number of figures =
Bayesian sequential change diagnosis
Sequential change diagnosis is the joint problem of detection and
identification of a sudden and unobservable change in the distribution of a
random sequence. In this problem, the common probability law of a sequence of
i.i.d. random variables suddenly changes at some disorder time to one of
finitely many alternatives. This disorder time marks the start of a new regime,
whose fingerprint is the new law of observations. Both the disorder time and
the identity of the new regime are unknown and unobservable. The objective is
to detect the regime-change as soon as possible, and, at the same time, to
determine its identity as accurately as possible. Prompt and correct diagnosis
is crucial for quick execution of the most appropriate measures in response to
the new regime, as in fault detection and isolation in industrial processes,
and target detection and identification in national defense. The problem is
formulated in a Bayesian framework. An optimal sequential decision strategy is
found, and an accurate numerical scheme is described for its implementation.
Geometrical properties of the optimal strategy are illustrated via numerical
examples. The traditional problems of Bayesian change-detection and Bayesian
sequential multi-hypothesis testing are solved as special cases. In addition, a
solution is obtained for the problem of detection and identification of
component failure(s) in a system with suspended animation
Regularized parametric system identification: a decision-theoretic formulation
Parametric prediction error methods constitute a classical approach to the
identification of linear dynamic systems with excellent large-sample
properties. A more recent regularized approach, inspired by machine learning
and Bayesian methods, has also gained attention. Methods based on this approach
estimate the system impulse response with excellent small-sample properties. In
several applications, however, it is desirable to obtain a compact
representation of the system in the form of a parametric model. By viewing the
identification of such models as a decision, we develop a decision-theoretic
formulation of the parametric system identification problem that bridges the
gap between the classical and regularized approaches above. Using the
output-error model class as an illustration, we show that this
decision-theoretic approach leads to a regularized method that is robust to
small sample-sizes as well as overparameterization.Comment: 10 pages, 8 figure
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