The small-data method is improved by false nearest neighbor method calculating embedding dimension.
通过用虚假临界点法计算嵌入维数可以使小数据量法更加完善。
In phase space reconstruction of time sequences, the selection of embedding dimension is important.
嵌入维是时间序列相空间重构中的基本参数。
The minimum embedding dimension of reconstruction is confirmed by the false nearest neighbours method.
利用伪最邻近点法确定相空间重构的最小嵌入维数。
Based on local linear prediction model of chaotic time series, short-term load forecasting method on multi-embedding dimension is presented.
基于混沌时间序列的局域线性预测模型，提出了多嵌入维的短期负荷预测方法。
The relationship of embedding dimension and delay time is discussed and a new concept namely the generalized embedding Windows is put forward.
论述相空间重构中延迟时间与嵌入维数之间的关系，提出广义嵌入窗长的概念。
Algorithms for searching the optimal embedding dimension and interval prediction are presented, which can be applied in practice with satisfactory.
讨论混沌时间序列的区间预测，给出了最优嵌入维数的搜索算法及区间预测算法，并应用于实例，取得较好效果。
By phase space reconstruction, choosing the most suitable delay time and embedding dimension in order to embed time series which reflect the demanding into the phase space.
通过相空间重构技术，选取合适的延迟时间和嵌入维数，将反映市场需求的时间序列嵌入到相空间中。
Based on the idea of looking at the behavior of near neighbors under changes in the reconstruction dimension, a new method to determine the proper minimum embedding dimension is constructed.
本文基于“增大重构维以减少虚邻点”的思想，构造了一种求合适最小嵌入维的方法。
Recognition rate is superior to the traditional PCA algorithm. Finally experiments analyze the relationship between neighbor K and the embedding dimension of algorithms SLLE to the recognition rate.
最后实验分析了SLLE算法近邻数K和嵌入维数对识别率的影响，得到了SLLE算法的最优近邻数K和低维嵌入维数。
The results of actual runoff prediction show that the proposed method could use information synthetically in multi-dimension embedding phase spaces, and effectively improve the prediction accuracy.
实例分析表明，相对于单嵌入维数法，多嵌入维数组合预测方法可以综合利用不同相空间中的有用信息，提高径流时间序列预测的精度。
The results of actual runoff prediction show that the proposed method could use information synthetically in multi-dimension embedding phase spaces, and effectively improve the prediction accuracy.
实例分析表明，相对于单嵌入维数法，多嵌入维数组合预测方法可以综合利用不同相空间中的有用信息，提高径流时间序列预测的精度。
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