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The dimension of the input vector is determined by using the false nearest neighbor approach.
For each time series, different delay time values for phase space reconstruction are considered and the optimum embedding dimension is determined using the false nearest neighbor (FNN) method.
A key point in this process is the computing of the time series embedding dimension using the False Nearest Neighbors (FNN) method.
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By using the false-nearest-neighbours method, we have argued that the deterministic component of solar wind plasma dynamics should be low-dimensional.
Be careful of using the false dilemma fallacy.
The nonlinear behaviors of SRBS were investigated by the reconstructing phase space, using the autocorrelation function and the false nearest neighbor method.
The false nearest neighbor method is used to determine the embedding dimension, and the principal component analysis (PCA) is used to reduce noise and dimension.
First, the mutual information method and the false nearest neighbor method were used to calculate parameters to reconstruct the original data.
If f d (k) > fD, the phase point y k NN is determined as the false nearest neighbor of the phase point y k, where fD is a threshold value used for verifying whether the nearest neighbor of phase point is false or not.
The present study introduces the false nearest neighbor (FNN) algorithm, a nonlinear dynamic-based method, to examine the spatial variability of streamflow over a region.
Furthermore, the percentage of the false nearest neighbors in all nearest neighbors of all phase points is defined as β(d).
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