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 visit the SynMath homepage of our partner company SynOptio.

This email address is being protected from spambots. You need JavaScript enabled to view it. February 2, 2005; October 18, 2009