Modeling of coal consumption rate based on wavelet neural network

Abstract

In this paper, two wavelet neural network (WNN) frames which depend on Morlet wavelet function and Gaussian wavelet function were established. In order to improve the efficiency of model training, the momentum term was applied to modify the weights and thresholds, and the output of the network was summed up by function transformation of output layer nodes. When the Gaussian Wavelet Neural Networks (GWNN) and Morlet Wavelet Neural Networks (MWNN) were applied to coal consumption rate (CCR) estimation in a thermal power plant, the results confirmed their potency in function approximation. In addition, the influence of learning rate on the models was also discussed through the orthogonal experiment.