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Found 10 papers, 0 papers with code
Learning linear modules in a dynamic network with missing node observations
The related optimization problem is solved using an Expectation-Maximization (EM) method, where we employ a Markov-chain Monte Carlo (MCMC) technique to reconstruct the unknown missing node information and the network dynamics.
The Generalized Cross Validation Filter
Generalized cross validation (GCV) is one of the most important approaches used to estimate parameters in the context of inverse problems and regularization techniques.
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.
Boosting as a kernel-based method
We show that boosting with this learner is equivalent to estimation with a special {\it boosting kernel} that depends on $K$, as well as on the regression matrix, noise variance, and hyperparameters.
A new kernel-based approach for overparameterized Hammerstein system identification
We show that the resulting scheme provides an estimate of the overparameterized vector that can be uniquely decomposed as the combination of an impulse response and $p$ coefficients of the static nonlinearity.
On the estimation of initial conditions in kernel-based system identification
Recent developments in system identification have brought attention to regularized kernel-based methods, where, adopting the recently introduced stable spline kernel, prior information on the unknown process is enforced.
Bayesian kernel-based system identification with quantized output data
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.
Blind system identification using kernel-based methods
The structure of the covariance matrix (or kernel) of such a process is given by the stable spline kernel, which has been recently introduced for system identification purposes and depends on an unknown hyperparameter.
Robust EM kernel-based methods for linear system identification
In this paper, we introduce a novel method to robustify kernel-based system identification methods.
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.
