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Machine learning stochastic design models

Matthews, P.C.

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Authors



Contributors

A.E Samuel
Editor

W.P. Lewis
Editor

Abstract

Due to the fluid nature of the early stages of the design process, it is difficult to obtain deterministic product design evaluations. This is primarily due to the flexibility of the design at this stage, namely that there can be multiple interpretations of a single design concept. However, it is important for designers to understand how these design concepts are likely to fulfil the original specification, thus enabling the designer to select or bias towards solutions with favourable outcomes. One approach is to create a stochastic model of the design domain. This paper tackles the issues of using a product database to induce a Bayesian model that represents the relationships between the design parameters and characteristics. A greedy learning algorithm is presented and illustrated using a simple case study.

Citation

Matthews, P. (2005). Machine learning stochastic design models. In A. Samuel, & W. Lewis (Eds.), 15th International Conference on Engineering Design, ICED05, 15-18 August 2005, Melbourne, Australia ; proceedings

Conference Name 15th International Conference on Engineering Design
Conference Location Melbourne, Australia
Start Date Aug 15, 2005
End Date Aug 18, 2005
Publication Date 2005-08
Deposit Date Jun 3, 2008
Publicly Available Date Jun 3, 2008
Series Title Proceedings of the 15th International Conference on Engineering Design.
Book Title 15th International Conference on Engineering Design, ICED05, 15-18 August 2005, Melbourne, Australia ; proceedings.
ISBN 08582578825
Keywords Conceptual and preliminary design, Search and optimisation, Graphical modelling, Machine learning, Bayesian networks.
Public URL https://durham-repository.worktribe.com/output/1167377
Publisher URL http://www.designsociety.org
Additional Information 15-18 Aug 2005. (CD-ROM)

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