Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism

Algorithm 2

.

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 .