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Classification is part of various applications and it is an important problem that represents active research topic. Support vector machine is one of the widely used and very powerful classifier. The accuracy of support vector machine highly depends on learning parameters. Optimal parameters can be efficiently determined by using swarm intelligence algorithms. In this paper, we proposed recent elephant...
Support vector regression (SVR) is a widely used technique for reliability prediction. The key issue for high prediction accuracy is the selection of SVR parameters, which is essentially an optimization problem. As one of the most effective evolutionary optimization methods, particle swarm optimization (PSO) has been successfully applied to tune SVR parameters and is shown to perform well. However,...
Performance of the support vector machine strongly depends on parameters settings. One of the most common algorithms for parameter tuning is grid search, combined with cross validation. This algorithm is often time consuming and inaccurate. In this paper we propose the use of stochastic metaheuristic algorithm, firefly algorithm, for effective support vector machine parameter tuning. The experimental...
In this paper, a novel online particle swarm optimization method is proposed to design speed and current controllers of sensorless vector controlled interior permanent magnet synchronous motor drives. The sliding mode observer is used for joint stator flux and rotor speed estimation. The stator resistance variation is compensated with a speed correction term which is derived from the estimation error...
This paper deals with the application of least squares support vector regression (LS-SVR) with radial basis function (RBF) kernel in dam crack forecasting. In the process of LS-SVR, we performed the standard grid search and particle swarm optimization (PSO) to tune hyperparameters of LS-SVR. The results demonstrate that our PSO approach can identify optimal or near optimal parameters faster than the...
Common used parameters selection method for support vector machines (SVM) is cross-validation, which is complicated calculation and takes a very long time. In this paper, a novel regularization parameter and kernel parameter tuning approach of SVM is presented based on quantum particle swarm optimization algorithm (QPSO). QPSO is a particle swarm optimization (PSO) with quantum individual that has...
Considering the prediction accuracy of health parameter in Engine Health Management (EHM) system used for aero-engine, a new compensation mechanism is proposed. The differences in the individuals of the same type engines and engine degradation after the use both will result in the modeling errors, while reducing the modeling errors is a key to improve the precision of fault diagnosis. A discrete series...
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