System identification techniques based on support vector machines without bias term
DOI10.1002/ACS.2404zbMath1284.93246OpenAlexW1494031781MaRDI QIDQ5408084
Publication date: 8 April 2014
Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/acs.2404
quadratic programmingconvex optimizationsystem identificationkernel functionrobust statisticsARMAX modelsupport vector machineapplicationactive-set methodoutput error model
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Weighted least squares support vector machines: robustness and sparse approximation
- Practical selection of SVM parameters and noise estimation for SVM regression
- Nonlinear black-box modeling in system identification: A unified overview
- Regularization networks and support vector machines
- Identification of MIMO Hammerstein models using least squares support vector machines
- 10.1162/153244302760185252
- The Steiglitz-McBride identification algorithm revisited--Convergence analysis and accuracy aspects
- 10.1162/1532443041827925
- Subspace identification of Hammerstein systems using least squares support vector machines
- Support Vector Method for RobustARMA System Identification
- Electric Load Forecasting
- An Improved Algorithm for Discrete $l_1 $ Linear Approximation
- Robust Statistics
This page was built for publication: System identification techniques based on support vector machines without bias term
