Using Monte-Carlo variance reduction in statistical tolerance synthesis
A statistical tolerance synthesis must analyse many sets of tolerances, each of which has a unique probability distribution. The Monte-Carlo technique that is typically used to evaluate the probability distribution must analyse large numbers of individual cases. The result is a huge number of indivi...
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| Veröffentlicht in: | Computer aided design 1997, Vol.29 (1), p.63-69 |
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| container_title | Computer aided design |
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| creator | Skowronski, Victor J Turner, Joshua U |
| description | A statistical tolerance synthesis must analyse many sets of tolerances, each of which has a unique probability distribution. The Monte-Carlo technique that is typically used to evaluate the probability distribution must analyse large numbers of individual cases. The result is a huge number of individual analyses, which is computationally expensive. This paper examines two Monte-Carlo variance reduction techniques, importance sampling and correlation, and proposes a method for using them in statistical tolerance synthesis. Correlation is used to reduce the error in the tolerance analyses. Importance sampling is used to estimate the sensitivity of an analysis to the tolerances so that a gradient based optimization algorithm can be used. |
| doi_str_mv | 10.1016/S0010-4485(96)00050-4 |
| format | Article |
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| ispartof | Computer aided design, 1997, Vol.29 (1), p.63-69 |
| issn | 0010-4485 1879-2685 |
| language | eng |
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| source | ScienceDirect Journals (5 years ago - present) |
| subjects | Algorithmics. Computability. Computer arithmetics Applied sciences Computer science control theory systems derivative estimation Exact sciences and technology optimization resampling sample reuse Simulation Software Theoretical computing |
| title | Using Monte-Carlo variance reduction in statistical tolerance synthesis |
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