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Finally, the proposed method is compared with SVM, feed forward neural network and type −1 Fuzzy logic system based classifier to show the efficacy of the proposed technique in classifying the Power quality events.
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Simulation work is performed on standard New England 39-bus benchmark system and the feasibility of implementation of the proposed SVM based classifier system for on-line security evaluation is also discussed.
A largely modified LCS, called Fuzzy Efficiency based Classifier System (FECS), originally designed by one of the authors, is used to solve this problem successfully.
The system integrates a support vector machine based classifier which extracts visual features on the fly and performs real-time classification and prediction.
Some dominant unique features are then extracted using digital signal processing tools to optimize neural network based classifier used in the existing system.
For this reason, we use weighted sparse representation based classifier to build a computational classification system for predicting protein interaction.
Within the same group, the MLPNN based classifier was more accurate than the LR based classifier.
Finally, DCNNs based classifier and multiclass support vector machines (SVMs) classifier are used for classification of single and complex PQDs.
The MLPNN based classifier outperformed the LR based counterpart.
The LDA based classifier showed poor internal validation (Figure S3).
This variant classifier possesses the advantages of both the traditional sparse representation based classifier and the Nearest Neighbor classifier.
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