Neural modeling of vapor compression refrigeration cycle with extreme learning machine

• SLFN is used to model the dynamics of vapor compression cycle.
• Regularized optimization of SLFN output weights are deduced
• Modeling results based on experiment data are given.

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.