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Found 11 papers, 1 papers with code
Coherence-based Input Design for Sparse System Identification
The maximum absolute correlation between regressors, which is called mutual coherence, plays an essential role in sparse estimation.
DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models
Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a challenging problem.
Non-causal regularized least-squares for continuous-time system identification with band-limited input excitations
In continuous-time system identification, the intersample behavior of the input signal is known to play a crucial role in the performance of estimation methods.
Learning sparse linear dynamic networks in a hyper-parameter free setting
We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting.
Robust exploration in linear quadratic reinforcement learning
This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function.
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.
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.
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.
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.
