L∞ IDENTIFICATION AND MODEL REDUCTION USING A LEARNING GENETIC ALGORITHM
Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 1125-1130 |
| Journal / Publication | IEE Conference Publication |
| Issue number | 427/2 |
| Publication status | Published - 1996 |
| Externally published | Yes |
Conference
| Title | UKACC International Conference on (CONTROL'96) |
|---|---|
| Location | University of Exeter |
| Place | United Kingdom |
| City | Exeter |
| Period | 2 - 5 September 1996 |
Link(s)
Abstract
This paper develops a Boltzmann learning enhanced genetic algorithm for L∞ norm based system identification and model reduction for robust control applications. Using this technique, both a globally optimised nominal model and an error bounding function for additive and multiplicative uncertainties can be obtained. It can also offer a tighter L∞ error bound and is applicable to both continuous and discrete-time systems.
Research Area(s)
- System identification, Model reduction, Robust control, Genetic Algorithms
Citation Format(s)
L∞ IDENTIFICATION AND MODEL REDUCTION USING A LEARNING GENETIC ALGORITHM. / Tan, Kay Chen; Li, Yun.
In: IEE Conference Publication, No. 427/2, 1996, p. 1125-1130.
In: IEE Conference Publication, No. 427/2, 1996, p. 1125-1130.
Research output: Journal Publications and Reviews › RGC 22 - Publication in policy or professional journal › peer-review
