System identification: theory for the user
System identification: theory for the user
ACM Transactions on Mathematical Software (TOMS)
ACM Transactions on Mathematical Software (TOMS)
Fast recursive identification of state space models via exploitation of displacement structure
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
N4SID: subspace algorithms for the identification of combined deterministic-stochastic systems
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Displacement structure: theory and applications
SIAM Review
Statistical analysis of novel subspace identification methods
Signal Processing - Special issue: subspace methods, part II: system identification
Subspace identification from closed loop data
Signal Processing - Special issue: subspace methods, part II: system identification
Matrix computations (3rd ed.)
Automatica (Journal of IFAC)
LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Basic Linear Algebra Subprograms for Fortran Usage
ACM Transactions on Mathematical Software (TOMS)
Journal of Computational and Applied Mathematics
Engineering and Scientific Computing with Scilab
Engineering and Scientific Computing with Scilab
Real-time estimation of multivariate dynamic time-varying market representation
CompSysTech '07 Proceedings of the 2007 international conference on Computer systems and technologies
Hi-index | 7.29 |
Basic algorithmic and numerical issues involved in subspace-based linear multivariable discrete-time system identification are described. A new identification toolbox--SLIDENT--has been developed and incorporated in the freely available Subroutine Library in Control Theory (SLICOT). Reliability, efficiency, and ability to solve industrial identification problems received a special consideration. Two algorithmic subspace-based approaches (MOESP and N4SID) and their combination, and both standard and fast techniques for data compression are provided. Structure exploiting algorithms and dedicated linear algebra tools enhance the computational efficiency and reliability. Extensive comparisons with the available computational tools based on subspace techniques show the better efficiency of the SLIDENT toolbox, at comparable numerical accuracy, and its capabilities to solve identification problems with many thousands of samples and hundreds of parameters.