### Abstract

Multiscale wavelet-based representation is a powerful data analysis and feature extraction tool. In this paper, this characteristic of multiscale representation is utilized to improve the prediction accuracy of nonlinear models by developing a multiscale nonlinear (MSNL) system identification algorithm. In particular, we consider the class of linear-in-the-parameters nonlinear models with known basis functions. The idea is to decompose the input-output data, construct multiple nonlinear models at multiple scales using the scaled signal approximations of the data, and then select among all MSNL models the one which best describes the process. The main advantage of the MSNL modeling algorithm is that it inherently accounts for the presence of noise in the data by the application of low pass Alters used in the multiscale decomposition, which in turn improves the model robustness to measurement noise in the data and thus enhances its prediction. This advantage of MSNL modeling is demonstrated using a reactor model with nonlinear reaction rate.

Original language | English |
---|---|

Title of host publication | Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC |

Pages | 5755-5760 |

Number of pages | 6 |

DOIs | |

Publication status | Published - 2007 |

Event | 46th IEEE Conference on Decision and Control 2007, CDC - New Orleans, LA, United States Duration: 12 Dec 2007 → 14 Dec 2007 |

### Other

Other | 46th IEEE Conference on Decision and Control 2007, CDC |
---|---|

Country | United States |

City | New Orleans, LA |

Period | 12/12/07 → 14/12/07 |

### Fingerprint

### ASJC Scopus subject areas

- Control and Systems Engineering
- Modelling and Simulation
- Control and Optimization

### Cite this

*Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC*(pp. 5755-5760). [4434311] https://doi.org/10.1109/CDC.2007.4434311

**Multiscale nonlinear system identification.** / Nounou, Mohamed N.; Nounou, Hazem N.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC.*, 4434311, pp. 5755-5760, 46th IEEE Conference on Decision and Control 2007, CDC, New Orleans, LA, United States, 12/12/07. https://doi.org/10.1109/CDC.2007.4434311

}

TY - GEN

T1 - Multiscale nonlinear system identification

AU - Nounou, Mohamed N.

AU - Nounou, Hazem N.

PY - 2007

Y1 - 2007

N2 - Multiscale wavelet-based representation is a powerful data analysis and feature extraction tool. In this paper, this characteristic of multiscale representation is utilized to improve the prediction accuracy of nonlinear models by developing a multiscale nonlinear (MSNL) system identification algorithm. In particular, we consider the class of linear-in-the-parameters nonlinear models with known basis functions. The idea is to decompose the input-output data, construct multiple nonlinear models at multiple scales using the scaled signal approximations of the data, and then select among all MSNL models the one which best describes the process. The main advantage of the MSNL modeling algorithm is that it inherently accounts for the presence of noise in the data by the application of low pass Alters used in the multiscale decomposition, which in turn improves the model robustness to measurement noise in the data and thus enhances its prediction. This advantage of MSNL modeling is demonstrated using a reactor model with nonlinear reaction rate.

AB - Multiscale wavelet-based representation is a powerful data analysis and feature extraction tool. In this paper, this characteristic of multiscale representation is utilized to improve the prediction accuracy of nonlinear models by developing a multiscale nonlinear (MSNL) system identification algorithm. In particular, we consider the class of linear-in-the-parameters nonlinear models with known basis functions. The idea is to decompose the input-output data, construct multiple nonlinear models at multiple scales using the scaled signal approximations of the data, and then select among all MSNL models the one which best describes the process. The main advantage of the MSNL modeling algorithm is that it inherently accounts for the presence of noise in the data by the application of low pass Alters used in the multiscale decomposition, which in turn improves the model robustness to measurement noise in the data and thus enhances its prediction. This advantage of MSNL modeling is demonstrated using a reactor model with nonlinear reaction rate.

UR - http://www.scopus.com/inward/record.url?scp=62749096779&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=62749096779&partnerID=8YFLogxK

U2 - 10.1109/CDC.2007.4434311

DO - 10.1109/CDC.2007.4434311

M3 - Conference contribution

AN - SCOPUS:62749096779

SN - 1424414989

SN - 9781424414987

SP - 5755

EP - 5760

BT - Proceedings of the 46th IEEE Conference on Decision and Control 2007, CDC

ER -