Accelerating genetic algorithm computation in tree shaped parallel computer
Journal of Systems Architecture: the EUROMICRO Journal
Incremental training of support vector machines using hyperspheres
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Predicting defect-prone software modules using support vector machines
Journal of Systems and Software
Computers and Industrial Engineering
Artificial Intelligence in Medicine
Computers and Electronics in Agriculture
Expert Systems with Applications: An International Journal
Implementing support vector regression with differential evolution to forecast motherboard shipments
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
Prediction of electromechanical equipments state nonlinear and non-stationary condition effectively is significant to forecast the lifetime of electromechanical equipments. In order to forecast electromechanical equipments state exactly, support vector regression optimized by genetic algorithm is proposed to forecast electromechanical equipments state. In the model, genetic algorithm is employed to choose the training parameters of support vector machine, and the SVR forecasting model of electromechanical equipments state with good forecasting ability is obtained. The proposed forecasting model is applied to the state forecasting for industrial smokes and gas turbine. The experimental results demonstrate that the proposed GA-SVR model provides better prediction capability. Therefore, the method is considered as a promising alternative method for forecasting electromechanical equipments state.