Magnetic Remanence Prediction of NdFeB Magnets Based on a Novel Machine Learning Intelligence Approach Using a Particle Swarm Optimization Support Vector Regression.
WenDe ChengPublished in: Int. J. Softw. Sci. Comput. Intell. (2014)
Keyphrases
- support vector regression
- particle swarm optimization
- machine learning
- magnetic field
- support vector machine
- load forecasting
- support vector
- hybrid model
- prediction model
- forecasting accuracy
- regression model
- support vector machine svm
- pso algorithm
- support vector classification
- response surface methodology
- particle swarm optimization algorithm
- autoregressive conditional heteroscedasticity
- kernel function
- hybrid genetic
- ann models
- genetic algorithm
- convergence speed
- multi objective
- data mining
- prediction error
- pattern recognition
- global optimization
- extreme learning machine
- training data
- forecasting model
- back propagation neural network
- neural network
- nonlinear regression
- computational intelligence
- model selection
- k nearest neighbor
- differential evolution
- wavelet neural network
- particle swarm
- feature selection
- optimization algorithm
- wavelet frame
- singular spectrum analysis
- power system