A new kernel-based approach to hybrid system identification

@article{Pillonetto2016ANK,
  title={A new kernel-based approach to hybrid system identification},
  author={G. Pillonetto},
  journal={Autom.},
  year={2016},
  volume={70},
  pages={21-31}
}
All the approaches for hybrid system identification appeared in the literature assume that model complexity is known. Popular models are e.g. piecewise ARX with a priori fixed orders. Furthermore, the developed numerical procedures have been tested only on simple systems, e.g. composed of ARX subsystems of order 1 or at most 2. This represents a major drawback for real applications. This paper proposes a new regularized technique for identification of piecewise affine systems, namely the hybrid… Expand
Identification of Probability weighted ARX models with arbitrary domains
Stable spline identification of linear systems under missing data
A bilevel programming framework for piecewise affine system identification
Estimation of Switched Markov Polynomial NARX models
Spectral Bayesian Estimation for General Stochastic Hybrid Systems
Hybrid System Identification by Incremental Fuzzy C-regression Clustering
...
1
2
3
...

References

SHOWING 1-10 OF 49 REFERENCES
A new kernel-based approach for linear system identification
Tuning complexity in kernel-based linear system identification: The robustness of the marginal likelihood estimator
A Bayesian approach to identification of hybrid systems
A bounded-error approach to piecewise affine system identification
A clustering technique for the identification of piecewise affine systems
Prediction error identification of linear systems: A nonparametric Gaussian regression approach
An algebraic geometric approach to the identification of a class of linear hybrid systems
Identification of Hybrid Systems: A Tutorial
...
1
2
3
4
5
...