### Abstract

In this paper, an evolutionary approach is proposed to obtain the discrete-time transfer function for uncertain continuous-time systems having interval uncertainties. Based on a worst-case analysis, the problem to derive the discrete-time model is first formulated as multiple mono-objective optimization problems for coefficients in the discrete model, and subsequently minimized and maximized via a proposed genetic algorithm to obtain the lower and upper bounds of the coefficient functions. The problem of non-linearly coupled coefficients with exponential nature occurred in the exact discrete-time transfer function is therefore circumvented while preserving the interval structure in the resulting discrete model by using this approach. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature which requires undertaking a large number of evolution processes, parallel computation for the proposed evolutionary approach in a MATLAB-based working environment is therefore proposed to accelerate the derivation process.

Original language | English |
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Title of host publication | Proceedings of the IASTED International Conference on Computational Intelligence |

Pages | 310-315 |

Number of pages | 6 |

Publication status | Published - 2005 Dec 1 |

Event | IASTED International Conference on Computational Intelligence - Calgary, AB, Canada Duration: 2005 Jul 4 → 2005 Jul 6 |

### Publication series

Name | Proceedings of the IASTED International Conference on Computational Intelligence |
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Volume | 2005 |

### Other

Other | IASTED International Conference on Computational Intelligence |
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Country | Canada |

City | Calgary, AB |

Period | 05/7/4 → 05/7/6 |

### Keywords

- Discrete modeling
- Discretization
- Genetic algorithms
- Interval plant
- Parallel computation
- Sampled-data systems
- Uncertain systems

### ASJC Scopus subject areas

- Engineering(all)

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## Cite this

*Proceedings of the IASTED International Conference on Computational Intelligence*(pp. 310-315). (Proceedings of the IASTED International Conference on Computational Intelligence; Vol. 2005).