Structural Damage Identification Based on Multi-Classifier Cooperative Training
A structural damage identification framework based on multi-classifiers co-training(MCCT)was proposed.The framework combined multilayer perceptron,MLP)and support vector machine,SVM)for collaborative training,and selected samples with high confidence from unlabeled samples to label pseudo-labels,thus expanding the sample training set.The power spectral density(PSD)of ac-celeration response was used as the input feature to identify structural damage.The results show that the collaborative training method can select samples with high confidence from unlabeled samples and provide more labeled samples for damage identification.Compared with MLP and SVM,the damage i-dentification accuracy of this method is improved by about 4.7％and 6.3％respectively under various working conditions.
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