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基于EAST超导托卡马克实验装置,给出了HALO电流计算方法和计算过程,并通过自由能方法模拟得出了等离子体电流、HALO电流在等离子体破裂过程中随时间的演化过程。 相似文献
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HALO���������µ�EAST������غɷ��� 总被引:3,自引:2,他引:1
在弄清产生HALO电流的物理机制的基础上,分析了EAST超导托卡马克装置的最大HALO电流及其引起的最大电磁载荷,并采用数值模拟方法,对真空室结构在HALO电流作用下的受力情况进行了分析。 相似文献
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In the reconstructed phase space, a novel local linear prediction model is proposed to predict chaotic time series. The parameters of the proposed model take the values that are different from those of the phase space reconstruction. We propose a criterion based on prediction error to determine the optimal parameters of the proposed model. The simulation results show that the proposed model can effectively make one-step and multistep prediction for chaotic time series, and the one-step and multi-step prediction accuracy of the proposed model is superior to that of the traditional local linear prediction. 相似文献
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Based on the Bayesian information criterion, this paper proposes the
improved local linear prediction method to predict chaotic time
series. This method uses spatial correlation and temporal
correlation simultaneously. Simulation results show that the
improved local linear prediction method can effectively make
multi-step and one-step prediction of chaotic time series and the
multi-step prediction performance and one-step prediction accuracy
of the improved local linear prediction method are superior to those
of the traditional local linear prediction method. 相似文献
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A new method is proposed to determine the optimal embedding
dimension from a scalar time series in this paper. This method
determines the optimal embedding dimension by optimizing the
nonlinear autoregressive prediction model parameterized by the
embedding dimension and the nonlinear degree. Simulation results
show the effectiveness of this method. And this method is applicable
to a short time series, stable to noise, computationally efficient,
and without any purposely introduced parameters. 相似文献
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Small-time scale network traffic prediction based on a local support vector machine regression model 总被引:2,自引:0,他引:2 下载免费PDF全文
In this paper we apply the nonlinear time series analysis method to
small-time scale traffic measurement data. The prediction-based
method is used to determine the embedding dimension of the traffic
data. Based on the reconstructed phase space, the local support
vector machine prediction method is used to predict the traffic
measurement data, and the BIC-based neighbouring point selection
method is used to choose the number of the nearest neighbouring
points for the local support vector machine regression model. The
experimental results show that the local support vector machine
prediction method whose neighbouring points are optimized can
effectively predict the small-time scale traffic measurement data
and can reproduce the statistical features of real traffic
measurements. 相似文献
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对大学物理基础性实验的教学现状做了初步的问卷调查,显示出很多学生对实验重视程度不够、缺乏兴趣、对教师依赖性大以及实验教学方式单一等,从而针对性地提出了一些教学建议. 相似文献
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