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| 1 | 基于核和灰度的区间灰数预测模型显示文摘在灰色系统理论研究领域,以'灰数'序列为建模对象的灰色预测模型的研究还较为缺乏。以区间灰数的'核'序列为基础建立预测模型,实现未来区间灰数'核'的预测,然后以'灰度不减公理'为理论依据,以'核'为中心拓展得出区间灰数的上界和下界;在不破坏区间灰数独立性和完整性的前提下,实现了区间灰数预测模型的构建;算例分析验证了该模型的有效性及实用性;区间灰数预测模型对丰富与完善灰色预测模型的理论体系、拓展灰色预测模型的应用范围,均具有十分重要的意义。 | 曾波 | 2011 | 系统工程与电子技术2011，33，4： | 27 |
| 2 | 基于核和灰度的灰色马尔可夫预测模型及应用显示文摘在处理预测问题时,常有原始数据为区间数组成的随机波动性较大的区间数列的状况。为进一步提高区间灰数预测精度,提出基于核和灰度的灰色马尔可夫预测模型。该方法以区间灰数核序列为依托建立预测模型,实现区间灰数核的预测;又根据“灰度不减公理”,由灰数核为中心延伸得出区间灰数的上下界;在保持区间灰数独立完整的前提下,构建了区间灰数预测模型,在此基础上用马尔可夫预测模型修正预测结果。该模型在航空货运量的趋势预测中显示马式链修正结果较区间灰数预测数据呈低估状态。结果有助于加强市场参与者对航空货运市场的宏观认识,并为经济决策行为提供参考。 | 王建华 查怡婷 王雪 熊峰 | 2020 | 系统工程与电子技术2020，42，2： | 7 |
| 3 | IUKF neural network modeling for FOG temperature drift显示文摘A novel neural network based on iterated unscented Kalman filter(IUKF) algorithm is established to model and compensate for the fiber optic gyro(FOG) bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter(UKF). The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measurement noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and measurement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise(-20-20 C) and drop(70-20 C)tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the backpropagation(BP) and UKF network models. | Feng Zha Jiangning Xu Jingshu Li Hongyang He | 2013 | Journal of Systems Engineering and Electronics2013，24，5： | 4 |
| 4 | 改进灰色马尔科夫模型在基坑预测中的研究显示文摘基坑预测问题关系到工程施工的安全,在施工过程中对基坑进行周密的监测和变性预测分析显得尤为重要。针对传统预测模型存在固有偏差和可靠性低的缺点,采用新陈代谢的原理对无偏灰色加权马尔科夫模型进行改进。该模型先用无偏灰色模型拟合系统的总体变化趋势,然后,对拟合残差进行马尔可夫状态划分,并根据各阶权重对不同步长的转移矩阵进行加权处理,用加权后的无偏灰色马尔科夫模型进行预测。在每一步的预测中,利用新陈代谢的原理不断更新建模所使用的数据。将该模型用于基坑沉降预测,并通过实例进行验证。实验表明:基于新陈代谢的无偏灰色加权马尔科夫模型提高了基坑沉降预测的精度和可靠性,预测精度与未改进模型相比提高了8.54%。 | 杨帆 赵增鹏 王小兵 | 2017 | 测绘与空间地理信息2017，40，7： | 4 |
| 5 | Partial Improvement of Traditional Grey-Markov Model and Its Application on Fault Prediction显示文摘Modeling experiences of traditional grey-Markov show that the prediction results are not accurate when analyzed data are rare and fluctuated.So it is necessary to revise or improve the original modeling procedure of the grey-Markov(GM)model.Therefore,a new idea is brought forward that the Markov theory is used twice,where the first time is to extend the original data and the second to calculate and estimate the residual errors.Then by comparing the original data sequence from a fault prediction case with the simulation sequence produced by the use of GM(1,1) and the new GM method,results are conforming to the original data.Finally,an assumption of GM model is put forward as the future work. | Li Yongping Jia Cili | 2017 | Transactions of Nanjing University of Aeronautics and Astronautics2017，34，4： | 1 |
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