| 最小二乘支持向量机在堆芯功率分布重构中的应用 |
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| 引用本文： | 彭星杰,李天涯,李庆,王侃.最小二乘支持向量机在堆芯功率分布重构中的应用[J].原子能科学技术,2015,49(6):1026-1031. |
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| 作者姓名： | 彭星杰 李天涯 李庆 王侃 |
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| 作者单位： | 1.清华大学 工程物理系，北京100084；2.中国核动力研究设计院 核反应堆系统设计技术重点实验室，四川 成都610041 |
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| 摘 要： | 应用最小二乘支持向量机(LS-SVM)进行了堆芯轴向功率分布重构的研究,通过6节堆内中子探测器的信号重构出堆芯轴向18个节块的功率。使用ACP-100模块式小堆的7 740套轴向功率分布对LS-SVM重构算法进行了验证,实验结果表明:LS-SVM算法的重构精度远优于交替条件期望(ACE)算法,且LS-SVM算法具有良好的鲁棒性。
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| 关 键 词： | 最小二乘支持向量机 功率分布重构 鲁棒性 |
Application of Least Square Support Vector Machine in Core Power Distribution Reconstruction |
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| PENG Xing-jie,LI Tian-ya,LI Qing,WANG Kan.Application of Least Square Support Vector Machine in Core Power Distribution Reconstruction[J].Atomic Energy Science and Technology,2015,49(6):1026-1031. |
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| Authors: | PENG Xing-jie LI Tian-ya LI Qing WANG Kan |
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| Affiliation: | 1.Department of Engineering Physics, Tsinghua University, Beijing 100084, China; 2.Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu 610041, China |
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| Abstract: | The application of the least square support vector machine (LS-SVM) to core axial power distribution reconstruction was researched, and 18-node powers were reconstructed from six-level in-core detector signals. Axial power distributions of 7 740 cases of ACP-100 modular reactor were used to verify the accuracy of the LS-SVM reconstruction method. The results show that the LS-SVM method performs much better than the alternating conditional expectation (ACE) method and the LS-SVM method has good robustness. |
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| Keywords: | least square support vector machine power distribution reconstruction robustness |
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