Name
Abstract | ||
|---|---|---|
In nonlinear system identification, Volterra kernel estimation based on regularized least squares can be performed by taking a Bayesian approach. In this framework, covariance structures which describe the Gaussian kernels are represented by a set of hyperparameters. The hyperparameters are traditionally tuned through a global optimization which maximizes their marginal likelihood with respect to ... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/LCSYS.2017.2719766 | IEEE Control Systems Letters |
Keywords | Field | DocType |
Kernel,Optimization,Estimation,Covariance matrices,Bayes methods,Noise measurement,Finite impulse response filters | Hyperparameter optimization,Mathematical optimization,Hyperparameter,Global optimization,Nonlinear system identification,Algorithm,Marginal likelihood,Volterra series,Optimization problem,Mathematics,Kernel density estimation | Journal |
Volume | Issue | ISSN |
1 | 2 | 2475-1456 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeremy Stoddard | 1 | 0 | 0.68 |
| James S. Welsh | 2 | 17 | 6.59 |
| Håkan Hjalmarsson | 3 | 1254 | 175.16 |