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.
Bibliographical noteFunding Information:
We are greatly indebted to our technical collaborators, to the members of the CERN-SL Division for the excellent performance of the LEP collider, and to the funding agencies for their support in building and operating the DELPHI detector. We acknowledge in particular the support of Austrian Federal Ministry of Science and Traffics, GZ 616.364/2-III/2a/98; FNRS–FWO, Belgium; FINEP, CNPq, CAPES, FUJB and FAPERJ, Brazil; Czech Ministry of Industry and Trade, GA CR 202/96/0450 and GA AVCR A1010521; Danish Natural Research Council; Commission of the European Communities (DG XII); Direction des Sciences de la Matière, CEA, France; Bundesministerium für Bildung, Wissenschaft, Forschung und Technologie, Germany; General Secretariat for Research and Technology, Greece; National Science Foundation (NWO) and Foundation for Research on Matter (FOM), The Netherlands; Norwegian Research Council; State Committee for Scientific Research, Poland, 2P03B06015, 2P03B1116 and SPUB/P03/178/98; JNICT-Junta Nacional de Investigação Cientı́fica e Tecnológica, Portugal; Vedecka grantova agentura MS SR, Slovakia, Nr. 95/5195/134; Ministry of Science and Technology of the Republic of Slovenia; CICYT, Spain, AEN96-1661 and AEN96-1681; The Swedish Natural Science Research Council; Particle Physics and Astronomy Research Council, UK; Department of Energy, USA, DE-FG02-94ER40817.
All Science Journal Classification (ASJC) codes
- Automotive Engineering
- Mechanical Engineering