This paper aims to propose a novel approach that can predicts the paste filling material strength of different components with high speed and accuracy because of the accuracy of the existing strength prediction methods is low .To address these problems, the deep belief network (DBN) which can extract the deep layer connection of input parameters is employed to establish the prediction model. In order to realize the optimum prediction performance, the quantum particle swarm optimization algorithm (QPSO) is employed to determine the number of hidden layer nodes and learning rate of DBN. The components of filling material and their dimension are employed as the input of prediction model based on DBN, the output result is the compressive strength of filling material. The experimental results comparing BP neural network ,RVM(Relevance vector machine,RVM)and support vector machine (SVM) show that prediction approach can obtain Significant improvements in prediction time and precision of the 1.89 s by 99.84%.
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张安安,邓芳明. 基于深度信念网络的磷石膏充填材料强度预测[J]. 科学技术与工程, 2020, 20(18): 7220-7225. Zhang Anan, Deng Fangming. Strength Prediction of Phosphogypsum Filling material Based on Improved Deep Belief Network[J]. Science Technology and Engineering,2020,20(18):7220-7225.