Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups of models into an aggregated model using fuzzy theory to obtain further performance improvements. First, three groups of l...

This paper describes the pattern recognition technique based on multiscale discrete wavelet transform(MDWT) and least square support vector machine (LS-SVM) for the classification of EEG signals. The different statistical features are extracted from each EEG signal corresponding to various seizer and nonsiezer brain functions, using MDWT. Further these sets of features are fed to the LS-SVM mul...

Since the aerobics is introduced into the college and university, it becomes popular in teachers and students. In order to develop the aerobics better and improve the level of the aerobics, it is necessary to predict the aerobics performance. Support vector machine method is one of the frequently-used prediction methods. In order to improve the performance of traditional LS-SVM, we put forward ...

A scheme of direct adaptive H∞ control based on least squares support vector machines (LS-SVM) is proposed for a class of nonlinear uncertain systems. In this method, LS-SVM is employed to construct the adaptive controller, and an on-line learning rule for the weighting vector and bias is derived. A parameter selection method based on the genetic algorithm (GA) is given for LS-SVM regression wi...

The least squares support vector machine (LS-SVM) has shown to exhibit excellent classification performance in many applications. In this paper, we propose an incremental and decremental LS-SVM based on updating and downdating the QR decomposition. It can efficiently compute the updated solution when data points are appended or removed. The experiment results illustrated that the proposed incre...

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on a financial exchange. The successful prediction of a stock's future price will maximize investor’s gains. This paper proposes a machine learning model to predict stock market price. The proposed algorithm integrates Particle swarm optimization (PSO) and least squ...

A simple and rapid method for the determination of Ba isotope abundances in water samples by inductively coupled plasma-optical emission spectrometry (ICP-OES) coupled with least-squares support vector machine regression (LS-SVM) is reported. By evaluation of emission lines of barium, it was found that the emission line at 493.408 nm provides the best results for the determination of Ba abundan...

A quantitative structure-activity relationship (QSAR) study was conducted for the prediction of inhibitory activity of 1-phenyl[2H]-tetrahydro-triazine-3-one analogues as inhibitors of 5-Lipoxygenase. The inhibitory activities of the 1-phenyl[2H]-tetrahydro-triazine-3-one analogues modeled as a function of molecular structures using chemometrics methods such as multiple linear regression (MLR) ...

Several studies have focused in the past on global land cover (LC) datasets harmonization and inter-comparison and have found significant inconsistencies. Despite the known discrepancies between existing products derived from medium resolution satellite sensor data, little emphasis has been placed on examining these disagreements to improve the overall classification accuracy of future land cov...

A simple and rapid method for the determination of 137Ba isotope abundances in water samples by inductively coupled plasma-optical emission spectrometry (ICP-OES) coupled with least-squares support vector machine regression (LS-SVM) is reported. By evaluation of emission lines of barium, it was found that the emission line at 493.408 nm provides the best results for the determination...