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Typically, the eigensolution computational time for obtaining the train sample data using PFEM is only 1% of that using the traditional FEM.
N max du 30 6 17 RB construction t RB offline (s) 362.8 s 6,733.2 s 2,794.2 s RB evaluation t RB online (s) 0.107 s 0.198 s 0.158 s FE evaluation t FE (s) 14.3 41.6 30.2 Computational speedup S 133 210 191 Break-even point Q BE 26 161 93 RB spaces have been built by means of the greedy procedure, using a tolerance ε tol RB = 10 − 2 and a uniform RB greedy train sample of size n train = 1, 000.
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Then, fault feature samples are divided into train samples and test samples.
Each training sample contains a class label.
(w, c(w)) is a training sample, and D is the set of all training samples.
The set is then treated as the initial training sample.
Suppose a training sample x and the corresponding label y.
The 60 remaining are used as a training sample.
The first set is used as a training sample set.
The accuracy of ANN depends heavily on the training sample.
The kernel width of each training sample is trained by two supervised training algorithms.
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