Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism
Input: sample set for training (including normal data and fault data): (),
is the eigenvalue, and is the classifying label.
Output: fault diagnosis model
(1) standardized the sample set ;
(2) parameter selection: choosing the kernel function and the kernel’s parameters;
(3) computer the Lagrangian coefficient ;
(4) obtained the support vector ;
(5) computer the threshold value ;
(6) establish the optimal separating hyperplane .
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