排序方式: 共有18条查询结果,搜索用时 15 毫秒
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研究了GM(1,1)模型的建模方法,建立了太阳电池阵输出功率的GM(1,1)预测模型,针对电池出输出功率具有随季节波动的变化趋势,提出了采用实时在线的方法,建立动态新息GM(1,1)预测模型,经实例预测验证,动态新息GM(1,1)模型可明显地提高预测精度,且能对电池阵输出功率的波动趋势正确预测。 相似文献
针对容积积分卡尔曼滤波(CQKF)受模型不确定性影响较大及需要精确已知噪声统计特性的缺点,提出了一种自适应强跟踪CQKF算法。该算法根据强跟踪滤波原理,引入渐消因子调整状态预测协方差矩阵,强迫残差序列正交,有效抑制了模型不确定性引起的滤波发散。在滤波过程中,利用Sage-Husa时变噪声统计估值器对过程噪声及量测噪声实时估计,提高了算法在未知时变噪声环境下的滤波精度。目标跟踪仿真实验验证了算法的有效性和鲁棒性。 相似文献
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动态测量系统的抗扰性预报算法 总被引:8,自引:0,他引:8
为了克服采样数据可能包含的野值对状态预报的不利影响,本文在动态测量系统的有界影响滤波技术的基础上提出了状态预报的两组抗扰性算法,并进行了仿真计算。 相似文献
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刘亚英 《中国空间科学技术》1997,17(4):35-40
分析研究了空间碎片数随太阳辐射流量F10.7的变化;给出预报F10.7长期变化的计算方法和预测空间碎片数的数学模型。结果显示:①强太阳活动造成空间碎片年增长率下降;②空间碎片数与太阳活动11年变化密切相关,相关数为0.9;③空间碎片增长率约为发射率的两倍;④若发射率保持不变,则到2020年,大于10cm的碎片数将达到14500;⑤若小碎片的增长为大碎片增长的两倍,则到2020年,大于1cm的碎片数可达125000。 相似文献
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证明了在Weibull分布和对数正态分布场合基于定数截尾样本利用参数的BLUE和BLIE构造枢轴量得到分布的各种可靠性指标的区间估计是相同的,纠正了文献[1,2]关于这个问题的错误认识。 相似文献
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G.Y.Smolkov 《空间科学学报》2005,25(5):345-350
Presented are the ideas and proposals in regards to the pooling of by RAS, SB, ISTP, and CSSAR,CAS toward coordinated usage of existing ground-based and orbiting helio-geophysical observatories, single large installations as well as creating, forecasting services and new observing facilities, in the interests of achieving a profitable activity of the China-Russia Joint Research Center on Space Weather (JRCSW). 相似文献
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Gordon Reikard 《Advances in Space Research (includes Cospar's Information Bulletin, Space Research Today)》2011
Space weather forecasts are currently used in areas ranging from navigation and communication to electric power system operations. The relevant forecast horizons can range from as little as 24 h to several days. This paper analyzes the predictability of two major space weather measures using new time series methods, many of them derived from econometrics. The data sets are the Ap geomagnetic index and the solar radio flux at 10.7 cm. The methods tested include nonlinear regressions, neural networks, frequency domain algorithms, GARCH models (which utilize the residual variance), state transition models, and models that combine elements of several techniques. While combined models are complex, they can be programmed using modern statistical software. The data frequency is daily, and forecasting experiments are run over horizons ranging from 1 to 7 days. Two major conclusions stand out. First, the frequency domain method forecasts the Ap index more accurately than any time domain model, including both regressions and neural networks. This finding is very robust, and holds for all forecast horizons. Combining the frequency domain method with other techniques yields a further small improvement in accuracy. Second, the neural network forecasts the solar flux more accurately than any other method, although at short horizons (2 days or less) the regression and net yield similar results. The neural net does best when it includes measures of the long-term component in the data. 相似文献