基于改进Kriging模型的主动学习可靠性分析方法
Active learning Kriging（ ALK） model is able to only approximate the performance function in a narrow region around the limit state surface.Therefore,the efficiency of reliability analysis is remarkably improved.However,most of the existing strategies build the ALK model based on a so-called DACE too...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Strength
2021-01-01
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| Series: | Jixie qiangdu |
| Online Access: | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.01.019 |
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| author | 陈哲 杨旭锋 程鑫 |
| author_facet | 陈哲 杨旭锋 程鑫 |
| author_sort | 陈哲 |
| collection | DOAJ |
| description | Active learning Kriging（ ALK） model is able to only approximate the performance function in a narrow region around the limit state surface.Therefore,the efficiency of reliability analysis is remarkably improved.However,most of the existing strategies build the ALK model based on a so-called DACE toolbox.DACE cannot obtain the global optimal parameter of a Kriging model and the training point chosen in each iteration cannot be the optimal one.In this paper,one famous global optimization,i.e.,the differential evolution algorithm is introduced to explore the optimal parameter of Kriging model and improve the accuracy of Kriging prediction information.As a result,the training point in each iteration is guaranteed to be the global optimal one and the efficiency of ALK model is largely improved. |
| format | Article |
| id | doaj-art-a3e12e08581f4596a2e37b7cd895127d |
| institution | Kabale University |
| issn | 1001-9669 |
| language | zho |
| publishDate | 2021-01-01 |
| publisher | Editorial Office of Journal of Mechanical Strength |
| record_format | Article |
| series | Jixie qiangdu |
| spelling | doaj-art-a3e12e08581f4596a2e37b7cd895127d2025-01-15T02:26:36ZzhoEditorial Office of Journal of Mechanical StrengthJixie qiangdu1001-96692021-01-0112913630609878基于改进Kriging模型的主动学习可靠性分析方法陈哲杨旭锋程鑫Active learning Kriging（ ALK） model is able to only approximate the performance function in a narrow region around the limit state surface.Therefore,the efficiency of reliability analysis is remarkably improved.However,most of the existing strategies build the ALK model based on a so-called DACE toolbox.DACE cannot obtain the global optimal parameter of a Kriging model and the training point chosen in each iteration cannot be the optimal one.In this paper,one famous global optimization,i.e.,the differential evolution algorithm is introduced to explore the optimal parameter of Kriging model and improve the accuracy of Kriging prediction information.As a result,the training point in each iteration is guaranteed to be the global optimal one and the efficiency of ALK model is largely improved.http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.01.019 |
| spellingShingle | 陈哲 杨旭锋 程鑫 基于改进Kriging模型的主动学习可靠性分析方法 Jixie qiangdu |
| title | 基于改进Kriging模型的主动学习可靠性分析方法 |
| title_full | 基于改进Kriging模型的主动学习可靠性分析方法 |
| title_fullStr | 基于改进Kriging模型的主动学习可靠性分析方法 |
| title_full_unstemmed | 基于改进Kriging模型的主动学习可靠性分析方法 |
| title_short | 基于改进Kriging模型的主动学习可靠性分析方法 |
| title_sort | 基于改进kriging模型的主动学习可靠性分析方法 |
| url | http://www.jxqd.net.cn/thesisDetails#10.16579/j.issn.1001.9669.2021.01.019 |
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