A gold grade prediction model was proposed by combining improved cloud models with improved RBF neural networks. Qualitative information was quantified using DS evidence theory and cloud models, and then quantum particle swarm optimization algorithm and RBF neural network were used to predict the gold grade in ores. The results indicate that the mean square error of this model is 0.009 2, the maximum error is 0.016 1, and the correlation coefficient is 0.940 2, the model can better preserve the qualitative information characteristics, the prediction effect of gold grade is good.

gold  /  grade  /  prediction  /  model  /  qualitative information  /  quantitative information  /  cloud model  /  RBF neural network