### 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 |

### Fingerprint

### Keywords

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

### ASJC Scopus subject areas

- Engineering(all)

### 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).

**Discrete modeling of uncertain continuous systems having an interval structure using genetic algorithms.** / Hsu, Chen Chien; Chang, Shih Chi; Kuo, Hsin Yen.

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

*Proceedings of the IASTED International Conference on Computational Intelligence.*Proceedings of the IASTED International Conference on Computational Intelligence, vol. 2005, pp. 310-315, IASTED International Conference on Computational Intelligence, Calgary, AB, Canada, 05/7/4.

}

TY - GEN

T1 - Discrete modeling of uncertain continuous systems having an interval structure using genetic algorithms

AU - Hsu, Chen Chien

AU - Chang, Shih Chi

AU - Kuo, Hsin Yen

PY - 2005/12/1

Y1 - 2005/12/1

N2 - 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.

AB - 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.

KW - Discrete modeling

KW - Discretization

KW - Genetic algorithms

KW - Interval plant

KW - Parallel computation

KW - Sampled-data systems

KW - Uncertain systems

UR - http://www.scopus.com/inward/record.url?scp=33751285678&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33751285678&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:33751285678

SN - 0889864810

SN - 9780889864818

T3 - Proceedings of the IASTED International Conference on Computational Intelligence

SP - 310

EP - 315

BT - Proceedings of the IASTED International Conference on Computational Intelligence

ER -