Abstract
This paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Evolutionary fuzzy modelling (EFM) is implemented in the context of global approximate optimization. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in the design process. Fuzzy inference system is central to identifying the input-output relationship in both methods. This paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process in EFM, and presents its generalization capabilities in terms of the number of fuzzy rules and training data. A three-bar truss design is first considered as a benchmark, and sizing of automotive A-pillar with rib structures for passenger protection is further explored in this context of EFM-based approximate optimization.
| Original language | English |
|---|---|
| Pages (from-to) | 339-355 |
| Number of pages | 17 |
| Journal | International Journal of Vehicle Design |
| Volume | 28 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2002 Jan 1 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Mechanical Engineering
Cite this
}
Evolutionary fuzzy modelling in global approximate structural optimization. / Lee, Jongsoo; Kim, Seungjin; Kang, Shinill.
In: International Journal of Vehicle Design, Vol. 28, No. 4, 01.01.2002, p. 339-355.Research output: Contribution to journal › Article
TY - JOUR
T1 - Evolutionary fuzzy modelling in global approximate structural optimization
AU - Lee, Jongsoo
AU - Kim, Seungjin
AU - Kang, Shinill
PY - 2002/1/1
Y1 - 2002/1/1
N2 - This paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Evolutionary fuzzy modelling (EFM) is implemented in the context of global approximate optimization. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in the design process. Fuzzy inference system is central to identifying the input-output relationship in both methods. This paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process in EFM, and presents its generalization capabilities in terms of the number of fuzzy rules and training data. A three-bar truss design is first considered as a benchmark, and sizing of automotive A-pillar with rib structures for passenger protection is further explored in this context of EFM-based approximate optimization.
AB - This paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Evolutionary fuzzy modelling (EFM) is implemented in the context of global approximate optimization. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in the design process. Fuzzy inference system is central to identifying the input-output relationship in both methods. This paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process in EFM, and presents its generalization capabilities in terms of the number of fuzzy rules and training data. A three-bar truss design is first considered as a benchmark, and sizing of automotive A-pillar with rib structures for passenger protection is further explored in this context of EFM-based approximate optimization.
UR - http://www.scopus.com/inward/record.url?scp=0036174282&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0036174282&partnerID=8YFLogxK
U2 - 10.1504/IJVD.2002.001994
DO - 10.1504/IJVD.2002.001994
M3 - Article
AN - SCOPUS:0036174282
VL - 28
SP - 339
EP - 355
JO - International Journal of Vehicle Design
JF - International Journal of Vehicle Design
SN - 0143-3369
IS - 4
ER -