Matthews, P.C. (2005) 'Machine learning stochastic design models.', in 15th International Conference on Engineering Design, ICED05, 15-18 August 2005, Melbourne, Australia ; proceedings. Melbourne, Australia: Design Society, DS35_29.44.
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.
|Item Type:||Book chapter|
|Keywords:||Conceptual and preliminary design, Search and optimisation, Graphical modelling, Machine learning, Bayesian networks.|
|Full text:||PDF - Accepted Version (146Kb)|
|Publisher Web site:||http://www.designsociety.org|
|Record Created:||03 Jun 2008|
|Last Modified:||31 May 2011 16:40|
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