Nonlinear Fitting Characteristics Analysis on Prediction Model of Adaptive Variable Weight Fuzzy RBF Neural Network

Abstract

For the number and width of the implicit layer RBF centers have a direct impact on the approximation capability of RBF neural network, clustering method is adopted to determine the radial basis function parameters, adaptive variable weight method is used to improve the conventional fuzzy RBF neural network learning algorithm, adaptive variable weight fuzzy RBF neural network prediction model is built, and simulation experiments are performed on its non-linear function approximation performance. Results show that the adaptive variable weight fuzzy RBF neural network prediction model has high accuracy in both single-input single-output nonlinear prediction and multiple-input multiple-output nonlinear prediction. Instability analysis results of surrounding rock in a massive metal mine gob area in southern China verify the effectiveness and practicality of the nonlinear fitting of the model.