LS-SVMLeast Square Support Vector Machine
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Therefore, the LS-SVM, which has strong fitting ability to complex nonlinear problems, is used to establish the forecasting model for each high frequency component, and obtain each prediction value and sum each prediction value up to obtain the final high frequency prediction value [y.sub.1](t);
Then, LS-SVM regression estimate using kernel function K(x, [x.sub.i]) can be expressed by formula (10).
By using the least squares support vector machine (LS-SVM) as the classifier, they obtained an overall accuracy of 89.66%, which is a bit lower than an accuracy of 93.10% obtained in the present study.
Feature Work set Classifiers Effectiveness [8] A Optimized SVM Accuracy = 99.2% Sensitivity = 98.43% Specificity = 100% [66] B NN Accuracy = 88.4% [10] A KNN Accuracy = 96.8% Sensitivity = 100% Specificity = 93.7% [9] A LS-SVM Accuracy = 99.7% Sensitivity = 99.6% Specificity = 99.8% [27] A SVM Accuracy = 79.71% [7] A LS-SVM Accuracy = 100% [19] B Fuzzy rule Accuracy = 84% Sensitivity = 79% Specificity = 89% [32] B Fuzzy rule Accuracy = 92.8% [58] B Fuzzy rule Accuracy = 81.2% [67] B Fuzzy rule and Accuracy = 84.44% ensemble classifier [55] A Random forest Sensitivity = 80% Specificity = 90% [44] A SVM with RBF Sensitivity = 73% Specificity = 87% [45] A SVM Sensitivity = 85% Specificity = 78%
The remaining part of this paper is organized as follows: Section 2 describes power system modeling, Section 3 contains the mathematical framework of the proposed TSI along with the model of the LS-SVM. In Section 4, simulation and results are presented, while Section 5 highlights major conclusions drawn from the work and also presents a solid milieu for future work.
The three-layer back propagation artificial neural network (BP-ANN) and LS-SVM are adopted, which have been applied successfully in downscaling temperature [12].
Deng, "Aeroengine abrasive identification based on IGA and LS-SVM," Lubrication, vol.
To compare the influences of different data preprocessing methods on calibration models, all the LS-SVM models are trained and validated using the same training and test sets.
In order to further validate the effectiveness of attribute reduction based on consistent covering rough set, the reduction results are recognized by Least Squares Support Vector Machine (LS-SVM) [29] and Relevance Vector Machine (RVM) [30].
In order to reduce computing time and enhance recognition accuracy, the least squares SVM (LS-SVM) was proposed.