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An enhancement of constraint feasibility in BPN based approximate optimization
- Source :
-
Computer Methods in Applied Mechanics & Engineering . Mar2007, Vol. 196 Issue 17-20, p2147-2160. 14p. - Publication Year :
- 2007
-
Abstract
- Back-propagation neural networks (BPN) have been extensively used as global approximation tools in the context of approximate optimization. A traditional BPN is normally trained by minimizing the absolute difference between target outputs and approximate outputs. When BPN is used as a meta-model for inequality constraint function, approximate optimal solutions are sometimes actually infeasible in a case where they are active at the constraint boundary. The paper explores the development of the efficient BPN based meta-model that enhances the constraint feasibility of approximate optimal solution. The BPN based meta-model is optimized via exterior penalty method to optimally determine interconnection weights between layers in the network. The proposed approach is verified through a simple mathematical function and a ten-bar planar truss problem. For constrained approximate optimization, design of rotor blade is conducted to support the proposed strategies. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 00457825
- Volume :
- 196
- Issue :
- 17-20
- Database :
- Academic Search Index
- Journal :
- Computer Methods in Applied Mechanics & Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 24045955
- Full Text :
- https://doi.org/10.1016/j.cma.2006.11.005