### Abstract

Original language | Undefined |
---|---|

Title of host publication | Proceedings of the 40th IEEE Conference on Decision and Control |

Place of Publication | Orlando, Florida, USA |

Publisher | IEEE CONTROL SYSTEMS SOCIETY |

Pages | - |

Number of pages | 6 |

ISBN (Print) | 0-7803-7063-5 |

DOIs | |

Publication status | Published - 4 Dec 2001 |

Event | 40th IEEE Conference on Decision and Control, CDC 2001 - Hyatt Regency Grand Cypress, Orlando, United States Duration: 4 Dec 2001 → 7 Dec 2001 |

### Publication series

Name | |
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Publisher | IEEE Control Systems Society |

### Conference

Conference | 40th IEEE Conference on Decision and Control, CDC 2001 |
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Abbreviated title | CDC |

Country | United States |

City | Orlando |

Period | 4/12/01 → 7/12/01 |

### Keywords

- METIS-205354
- iterative methods
- Smoothing methods
- covariance matrices
- Kalman filters
- Jacobian matrices
- State estimation
- multivariable systems
- State-spacemethods
- Parameter estimation
- nonlinear filters
- Linear systems
- IR-37644

### Cite this

*Proceedings of the 40th IEEE Conference on Decision and Control*(pp. -). Orlando, Florida, USA: IEEE CONTROL SYSTEMS SOCIETY. https://doi.org/10.1109/.2001.980959

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*Proceedings of the 40th IEEE Conference on Decision and Control.*IEEE CONTROL SYSTEMS SOCIETY, Orlando, Florida, USA, pp. -, 40th IEEE Conference on Decision and Control, CDC 2001, Orlando, United States, 4/12/01. https://doi.org/10.1109/.2001.980959

**Identification of a weighted combination of multivariable state space systems from input and output data.** / Verdult, V.; Verhaegen, M.H.G.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Identification of a weighted combination of multivariable state space systems from input and output data

AU - Verdult, V.

AU - Verhaegen, M.H.G.

PY - 2001/12/4

Y1 - 2001/12/4

N2 - Discusses a method for the determination of a weighted combination of local linear state-space systems from input and output data. The method is iterative and each iteration consists of two steps. The first step is to determine the weighting functions given the local models. This problem is solved by using an extended Kalman smoother. The second step is to identify the local models given the weights. For this step we optimize a cost function that represents a tradeoff between local and global learning. For this optimization we use a gradient search method in combination with an appropriate projection in the parameter space to deal with similarity transformations

AB - Discusses a method for the determination of a weighted combination of local linear state-space systems from input and output data. The method is iterative and each iteration consists of two steps. The first step is to determine the weighting functions given the local models. This problem is solved by using an extended Kalman smoother. The second step is to identify the local models given the weights. For this step we optimize a cost function that represents a tradeoff between local and global learning. For this optimization we use a gradient search method in combination with an appropriate projection in the parameter space to deal with similarity transformations

KW - METIS-205354

KW - iterative methods

KW - Smoothing methods

KW - covariance matrices

KW - Kalman filters

KW - Jacobian matrices

KW - State estimation

KW - multivariable systems

KW - State-spacemethods

KW - Parameter estimation

KW - nonlinear filters

KW - Linear systems

KW - IR-37644

U2 - 10.1109/.2001.980959

DO - 10.1109/.2001.980959

M3 - Conference contribution

SN - 0-7803-7063-5

SP - -

BT - Proceedings of the 40th IEEE Conference on Decision and Control

PB - IEEE CONTROL SYSTEMS SOCIETY

CY - Orlando, Florida, USA

ER -