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Minimal function calls approach with on-line learning and dynamic weighting for computationally intensive design optimization

Minimal function calls approach with on-line learning and dynamic weighting for computationally intensive design optimization
Minimal function calls approach with on-line learning and dynamic weighting for computationally intensive design optimization
Design/optimisation processes requiring intensive finite-element computation can be made significantly more efficient, while preserving good accuracy, by combining the Response Surface Methodology with on-line learning and dynamic weighting. The paper presents such a new development and uses the multi-parameter design of a brushless pm motor to illustrate the approach.
0018-9464
3423-3426
Sykulski, J.K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Al-Khoury, A.H.
0707c035-c6af-49cf-9773-dd9fc1380a09
Goddard, K.F.
fe2a2194-8b55-43c1-bdca-341691b71b2d
Sykulski, J.K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Al-Khoury, A.H.
0707c035-c6af-49cf-9773-dd9fc1380a09
Goddard, K.F.
fe2a2194-8b55-43c1-bdca-341691b71b2d

Sykulski, J.K., Al-Khoury, A.H. and Goddard, K.F. (2001) Minimal function calls approach with on-line learning and dynamic weighting for computationally intensive design optimization. IEEE Transactions on Magnetics, 37 (5), 3423-3426. (doi:10.1109/20.952628).

Record type: Article

Abstract

Design/optimisation processes requiring intensive finite-element computation can be made significantly more efficient, while preserving good accuracy, by combining the Response Surface Methodology with on-line learning and dynamic weighting. The paper presents such a new development and uses the multi-parameter design of a brushless pm motor to illustrate the approach.

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Published date: September 2001
Organisations: EEE

Identifiers

Local EPrints ID: 255902
URI: http://eprints.soton.ac.uk/id/eprint/255902
ISSN: 0018-9464
PURE UUID: 0535a308-e230-48fc-98bf-4e978fa409c5
ORCID for J.K. Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

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Date deposited: 03 Jun 2001
Last modified: 08 Apr 2020 00:24

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