Gradientless shape optimization using artificial neural networks.
IR@AMPRI: CSIR-Advanced Materials and Processes Research Institute, Bhopal
View Archive Info| Field | Value | |
| Title |
Gradientless shape optimization using artificial neural networks.
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| Creator |
D. K. , Sehgal
K. , Pathak |
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| Subject |
Lightweight Materials
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| Description |
In this paper a new zero order method of structural shape optimization, in which material shrinks or grows perpendicular to the design boundary, has been proposed in order to satisfy fully stressed design criteria. To avoid
mesh distortion that results in undesirable shape, design
element concept and for nodal movement and convergence checking, fuzzy set theory have been used. To accelerate
the convergence, artificial neural networks are employed.
The proposed approach, named as GSN technique, has been incorporated in a FORTRAN software GSOANN. Using this software shape optimization of four structures are carried out. It is demonstrated that proposed technique overcomes most of the shortcomings of mundane zero order methods.
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| Date |
2010
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| Type |
Article
PeerReviewed |
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| Identifier |
D. K. , Sehgal and K. , Pathak (2010) Gradientless shape optimization using artificial neural networks. Struct Multidisc Optim,, 41. pp. 699-709.
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| Relation |
http://ampri.csircentral.net/406/
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