Abstract
In general, structure of a system to be identified is unknown for users a priori. This makes the model complex and high order structure. In this paper, we introduce the asymptotic method (ASYM) to deal with the problem. ASYM calculates a high-order model using the well-known least squares method, then reduces the identified model to a simple one. For this model reduction, various model reduction techniques such as balanced realization and output error reduction were developed. In this paper, a new method to reduce the high-order model using the particle swarm optimization in the frequency domain is proposed. Effectiveness of the proposed method is examined through numerical examples.
| Original language | English |
|---|---|
| Title of host publication | ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings |
| Pages | 4499-4502 |
| Number of pages | 4 |
| Publication status | Published - 2009 Dec 1 |
| Event | ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan Duration: 2009 Aug 18 → 2009 Aug 21 |
Publication series
| Name | ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings |
|---|
Other
| Other | ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 |
|---|---|
| Country | Japan |
| City | Fukuoka |
| Period | 09/8/18 → 09/8/21 |
Keywords
- Asymptotic method
- Curve fitting
- High-order estimation
- Model reduction
- Particle swarm optimization
- System identification
ASJC Scopus subject areas
- Information Systems
- Control and Systems Engineering
- Industrial and Manufacturing Engineering
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Cite this
Muroi, H., & Adachi, S. (2009). Asymptotic system identification method based on particle swarm optimization. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings (pp. 4499-4502). [5333057] (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).