| 摘要: |
| 以探索知识重用在固体火箭发动机设计中的应用为目的,引入知识工程及模糊数学的思想,将一般模糊极小极大神经网络用于完成固体发动机设计过程中结构形式选择,实现了固体发动机结构形式选择的自动化及量化描述。首先给出了基于知识重用的固体发动机结构形式选择模型描述;然后建立了固体发动机结构选项一般模糊极小极大神经网络;最后以具体型号总体设计为例,完成结构形式选择,得到了包含实际结果的多个可行方案。该方法提高了设计过程自动化水平,且可提供多个不同的结构选择可行方案供设计人员参考。 |
| 关键词: 知识工程+ 知识重用+ 一般模糊极小极大神经网络+ 固体推进剂火箭发动机 结构模式识别+ |
| DOI： |
| 分类号:V435 |
| 基金项目: |
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| Solid rocket motor structure selection based on knowledge reuse |
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GU Jian-guang1, ZHANG Wei-hua2, WANG Zhong-wei3
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1.Inst.of Aerospace and Material Engineering,National Univ. of Defense Technology,Changsha 410073,China;2.Inst.of Aerospace and Material Engineering,National Univ. of Defense Technology,Changsha 410074,China;3.Inst.of Aerospace and Material Engineering,National Univ. of Defense Technology,Changsha 410075,China
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| Abstract: |
| To discuss the application of knowledge reuse in solid rocket motor(SRM) design,general fuzzy min-max(GFMM) neural network was used for structure selection of SRM design and knowledge-based engineering and fuzzy sets theory were studied as well as automatism and quantification of SRM structure selection were realized.Firstly,the model based on knowledge reuse of SRM structure selection was presented.Secondly,GFMM neural network for SRM structure selection was created.Finally,structure selection was finished in a SRM system design by the GFMM neural network.Many feasible results of structure selection were gained,which included the actual result.This technology can improve the degree of automatism and quantification of SRM structure selection,all of different feasible results are provided for SRM designer. |
| Key words: Knowledge-based engineering+ Knowledge reuse+ General fuzzy min-max neural network+ Solid propellant rocket engine Structure pattern recognition+ |
