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一种改进高斯过程的回归建模方法 被引量：5

Improved Gaussian process regression modeling method
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摘要 针对传统高斯过程对大样本数据的训练时间复杂度过高的问题,提出一种基于训练子样本优化选取的改进高斯过程回归建模方法.首先采用一种贪婪正向选择算法选择部分样本,并用所选的样本代替原训练样本来进行模型的学习;然后根据最大化似然函数并采用共轭梯度方法优化选取的子样本和超参数,得到更合适的参数;最后用优化后的参数进行改进高斯过程的建模和预测.仿真实验将改进高斯过程方法应用到海上远程精确打击(LPSS)体系作战效能评估问题中,得到了比传统高斯过程更好的预测结果,并显著降低了效能评估模型的时间复杂度. To reduce the training time complexity of Gaussian process for large sample data, an improved Gaussian process regression modeling method based on the training sub-samples selected was proposed. Firstly, some samples were selected with the greedy forward selection algorithm and the o- riginal training samples were replaced for learning model. Secondly, the likelihood function was maxi- mized in order to optimize sub-samples and hyper-parameters with conjugate gradient method. Finally, the optimized parameters were applied to model and forecast improved Gaussian process. The simulation results indicate that the improved Gaussian process can be used effectively in combating the ef fectiveness of the long range precision sea strike （LPSS） and shorten the training time significantly in modeling.
作者 张乐 刘忠 张建强 任雄伟
机构地区 海军工程大学电子工程学院 武汉军械士官学校
出处 《华中科技大学学报（自然科学版）》 EI CAS CSCD 北大核心 2013年第10期115-118,共4页 Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金 总装备部'十二五'预研项目(5133303)
关键词 高斯过程 改进 回归模型 贪婪正向选择算法 作战效能评估 Gaussian process improvement regression model greedy forward selection algorithm operational effectiveness evaluation
分类号 TP13 [自动化与计算机技术—控制理论与控制工程]
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参考文献14

  • 1Kou P, Gao F, Guan X, et al. Prediction intervals for wind power forecasting: using sparse warped Gaussian process [C] //Power and Energy Society General Meeting. San Diego: IEEE, 2012: 1-8.
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二级参考文献23

  • 1杨秀媛,肖洋,陈树勇.风电场风速和发电功率预测研究[J].中国电机工程学报,2005,25(11):1-5. 被引量：592
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共引文献99

同被引文献20

引证文献5

二级引证文献27

华中科技大学学报（自然科学版）
2013年 第10期

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