Application of General Regression Neural Network to Vibration Trend Prediction of Rotating Machinery

摘要

The General Regression Neural Network (GRNN) is briefly introduced. The BIC method for determining the order of Auto Regression (AR) model is employed to select the number of input neurons, and the Genetic Algorithm is applied to calculate the optimal smoothing parameter. The GRNN is used to predict the vibration time series of a large turbo-compressor, and its performance is compared with that of Radial Basis Function Neural Network (RBFNN), Back Propagation Neural Network (BPNN), and AR. It is indicated that the GRNN is more appropriate for the prediction of time series than the others, and is qualified even with sparse sample data.