using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme …-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly … limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression …

Markov Chain Monte Carlo (MCMC), Random Forest (RF), Support Vector Machine (SVM), neural networks (NNET) and generalized …

(FFOA), simulated annealing algorithm (SA), cross factor (CF) and support vector machine (SVM) to forecast the short … system in Kunming, China as a case study and uses the CF-SA-FFOA-SVM algorithm to predict the gas consumption and compares … discussed. The following conclusions are drawn: (1) compared with other forecasting models, CF-SA-FFOA-SVM model has higher gas …

order to promote the learning ability of the two support vector machines, a new kernel is derived for them. Findings (1) A …

machines (SVM) is proposed to find the Pareto front consisting of a set of possible solutions. The Pareto solutions are further …

Province, China, using support vector machine (SVM). Seven conditioning factors of elevation, slope, aspect, distance to roads … daily and cumulative rainfall data were employed as input data in the SVM modeling. The training dataset was constructed …-validation and receiver operation characteristic were applied to evaluate the performances of the SVM model. The summarized area …

the input data. In this study, a SVM based method was intended to adopt GCHP system for efficient modeling. The Lin-kernel … (cov) value is 0.077295, and mean error function (MEF) value is 0.507437 for the proposed Lin-kernel SVM method. The … vector machine (SVM) method. A GCHP system is a multi-variable system that is hard to model by conventional methods. As …