Neural Networks
Sales forecasting using extreme learning machine with applications in fashion retailing
Decision Support Systems
Quantitative Information Fusion for Hydrological Sciences
Quantitative Information Fusion for Hydrological Sciences
Universal approximation using incremental constructive feedforward networks with random hidden nodes
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
In this paper, a single-hidden layer feed-forward neural network (SLFN) is used to model the dynamics of the vapor compression cycle in refrigeration and air-conditioning systems, based on the extreme learning machine (ELM). It is shown that the assignment of the random input weights of the SLFN can greatly reduce the training time, and the regularization based optimization of the output weights of the SLFN ensures the high accuracy of the modeling of the dynamics of vapor compression cycle and the robustness of the SLFN against high frequency disturbances. The new SLFN model is tested with the real experimental data and compared with the ones trained with the back propagation (BP), the support vector regression (SVR) and the radial basis function neural network (RBF), respectively, with the results that the high degree of prediction accuracy and strongest robustness against the input disturbances are achieved.