For maximum convenience, easy-to-use interface M-functions are included in the identification toolbox, explicitly addressing some of supported features. Whenever possible, these M-functions allow to work with system objects defined in the MATLAB Control Toolbox.
The following table contains the list of implemented M-functions for linear and Wiener systems:
| findR | Input-output data preprocessing, using Cholesky or (fast) QR factorization and MOESP or N4SID identification techniques, and estimating the system order |
| findABCD | System matrices and Kalman gain estimation, using MOESP, N4SID, or their combination |
| findAC | Estimating the matrices A and C, using MOESP or N4SID |
| findBDK | Estimating the matrices B, D, and Kalman gain K (given A and C), using MOESP or N4SID |
| findx0BD | Estimating the initial state and/or the matrices B and D, given the matrices A, C, and a set of input-output data |
| inistate | Estimating the initial state, given the system matrices, and a set of input-output data |
| slmoesp | System matrices and the Kalman gain estimation, using MOESP technique |
| sln4sid | System matrices and the Kalman gain estimation, using N4SID technique |
| slmoen4 | System matrices and the Kalman gain estimation, using combined MOESP and N4SID techniques: A and C found via MOESP, and B and D, via N4SID |
| slmoesm | System matrices, the Kalman gain, and initial state estimation, using combined MOESP and system simulation techniques |
| dsim | Output response of a linear discrete-time system (much faster than the MATLAB function lsim) |
| o2s | Conversion of a linear discrete-time system given in the output normal form to a state-space representation |
| s2o | Conversion of a state-space representation of a linear discrete-time system into the output normal form |
| NNout | Output response of a set of neural networks used to model the nonlinear part of a Wiener system |
The first five M-functions allow to flexibly identify various system and covariance matrices for linear systems. The M-functions slmoesp, sln4sid, slmoen4, and slmoesm are method-oriented, and they also enable to efficiently estimate models of various orders.
The MEX-functions are more difficult to use than the provided M-functions, but allow a greater flexibility. They are called by the M-functions. The following table contains the list of MEX-files for linear and Wiener systems:
| order | Input-output data preprocessing, possibly sequentially, and finding an estimate of the system order |
| sident | System matrices, Kalman predictor gain, and covariance matrices estimation, using MOESP, N4SID, or their combination |
| findBD | Estimating the initial state and/or the matrices B and D, using A, C, and the input and output trajectories |
| ldsim | Output response of a linear discrete-time system (much faster than the MATLAB function lsim) |
| onf2ss | Conversion of a linear discrete-time system given in the output normal form to a state-space representation |
| ss2onf | Conversion of a state-space representation of a linear discrete-time system into the output normal form |
| widentc | Estimating a discrete-time model of a Wiener system using a neural network approach and a Levenberg-Marquardt algorithm (with a Cholesky-based, or a conjugate gradients solver) |
| wident | Estimating a discrete-time model of a Wiener system using a neural network approach and a MINPACK-like Levenberg-Marquardt algorithm |
| Wiener | Output response of a Wiener system |
The MEX-files above provide interfaces to the main user-callable or computational routines for linear and Wiener systems identification, and cover all functionality available in the corresponding SLICOT identification routines.
Executable SLICOT MEX-files are provided for recent MATLAB releases running under WINDOWS and Linux. Demonstration packages can also be provided.
For purchasing licenses of SLICOT-based MATLAB Toolboxes, please contact the SLICOT Administration.
Vasile Sima, February 2, 2005; October 18, 2009
