利用伪最邻近点法确定相空间重构的最小嵌入维数。
The minimum embedding dimension of reconstruction is confirmed by the false nearest neighbours method.
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。
The small-data method is improved by false nearest neighbor method calculating embedding dimension.
指出递归网络要实现逼近，需考虑初始条件、嵌入维数、逼近时效等因素。
And point out that in order to realize the approximation of recurrent networks, the initial conditions, embed dimension and approximation effects must be considered.
分别采用互信息方法确定重构最佳时延和和关联积分法确定重构嵌入维数。
Mutual information method and correlation integral method were presented to get the two optimal parameters.
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。
The relationship of embedding dimension and delay time is discussed and a new concept namely the generalized embedding Windows is put forward.
根据单变量时间序列的混沌预测方法，只要嵌入维数和延迟时间选择得合理，便能进行精确的预测。
Based on the prediction method of univariate time series, and according to the proper selection of dimension and delay time, the time series can be predicted precisely.
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。
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.
该方法联合考虑了嵌入滞时和嵌入维数，可同时计算出嵌入滞时和嵌入窗宽，避免了以往确定嵌入窗宽的主观性。
The correlation integral method can estimate both the time delay and the embedding window, and can avoid subjectivity in calculating the embedding window.
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。
Algorithms for searching the optimal embedding dimension and interval prediction are presented, which can be applied in practice with satisfactory.
通过选择合适的嵌入维数计算出的关联维数能够反映系统的真实特性，所以关联维数可以作为削片机检测的一个特征参量。
By choosing suitable insertion dimensions, the correlation dimensions can reflect the real dynamic characteristics of the system and correlation dimensions cou...
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。
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.
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.
数值测试表明，按混沌理论优选的延时时间和嵌入相空间的维数一般不是负荷预测的最适合参数。
Numerical simulation test shows that the delay time and embedded space dimension given by chaos theory usually are not the best ones for load forecasting.
数值测试表明，按混沌理论优选的延时时间和嵌入相空间的维数一般不是负荷预测的最适合参数。
Numerical simulation test shows that the delay time and embedded space dimension given by chaos theory usually are not the best ones for load forecasting.
应用推荐