Support Vector Machine (SVM) is a supervised machine learning algorithm, which is used for robust and accurate classification. Despite its advantages, its classification speed deteriorates due to its large number of support vectors when dealing with large scale problems and dependency of its performance on its kernel parameter. This paper presents a kernel parameter optimization algorithm for Support Vector Machine (SVM) based on Sliding Mode Control algorithm in a closed-loop manner. The proposed method defines an error equation and a sliding surface, iteratively updates the Radial Basis Function (RBF) kernel parameter or the 2-degree polynomial kernel parameters, forcing SVM training error to converge below a threshold value. Due to the closed-loop nature of the proposed algorithm, key features such as robustness to uncertainty and fast convergence can be obtained. To assess the performance of the proposed technique, ten standard benchmark databases covering a range of applications were used. The proposed method and the state-of-theart techniques were then used to classify the data. Experimental results show the proposed method is significantly faster and more accurate than the anchor SVM technique and some of the most recent methods. These achievements are due to the closed-loop nature of the proposed algorithm, which significantly has reduced the data dependency of the proposed method.INDEX TERMS Support vector machine, Sliding mode control, RBF kernel, 2-degree polynomial kernel, Optimal parameter, Classification speed.
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Extensive use of wind turbine (WT) systems brings remarkable challenges to the stability and safety of the power systems. Due to the difficulty and complexity of modeling such large plants, the model-independent strategies are preferred for the control of the WT plants which eliminates the need to model identification. This current work proposes a novel model-independent control methodology in the rotor side converter (RSC) part to ameliorate low voltage ride through (LVRT) ability especially for the doubly-fed induction generator (DFIG) WT. A novel model-independent nonsingular terminal sliding mode control (MINTSMC) was developed based on the principle of the ultra-local pattern. In the suggested controller, the MINTSMC scheme was designed to stabilize the RSC of the DFIG, and a sliding-mode supervisor was adopted to determine the unknown dynamics of the proposed system. An auxiliary dual input interval type 2 fuzzy logic control (DIT2-FLC) was established in a model-independent control structure to remove the estimation error of the sliding mode observer. Real-time examinations have been carried out using a Real-Time Model in Loop (RT-MiL) for validating the applicability of the proposed model-independent control in a real-time platform. To evaluate the usefulness and supremacy of the MINTSMC based DIT2-FLC, the real-time outcomes are compared with outcomes of RSC regulated conventional PI controller and MINTSMC controller.
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