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Features dimension significantly affects ANN training performance as well as generalization ability.
Hence, we utilize the principal component analysis (PCA) approach to reduce the features' dimension [31].
Taking voltage trajectory of generator bus as an example, the principle of determining preferable features dimension is detailed as follows.
However, it is worth noting that features dimension which depends upon the determination of ∆T affects CUGs prediction precision.
As discussed above, the features dimension determined by ∆T according to (3) is highly related to the prediction precision.
That means, for a single generator n, a set of ANNs denoted as: Open image in new window Fig. 2 Illustration of confirming features dimension.
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The features, dimensions and organisation of internal elements in freestall dairy barns are decisive in determining the layout design.
Therefore, feature dimension is reduced efficiently.
where d is the feature dimension number and D is the total number of feature dimension.
Next, we reduce the feature dimension by PCA dimension reduction.
The first assumption is about the dependency of certain feature dimension to the other feature dimensions, and the second is about the RTF of feature dimension.
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