基于粒子群算法的深度置信网络焙烧过程可溶锌率预测
Prediction of Soluble Zinc Rate during Roasting Process Based on Particle Swarm Optimization in Deep Belief Network
摘要: 针对锌冶炼焙烧过程焙砂质量衡量指标可溶锌率难以在线测量的问题，提出采用深度置信网络算法(Deep Belief Network, DBN)预测可溶锌率。但DBN网络结构是影响其预测性能的重要因素，合适的网路结构又难以确定，故提出先用信息熵法确定合适的隐层层数，再用PSO算法对隐层节点数和学习率进行优化，最终确定DBN网路结构。通过数据集仿真和可溶锌率预测实际应用，对该方法进行验证，与BP神经网络、RBF神经网络预测模型进行对比。结果表明，用信息熵法和PSO算法优化确定的DBN网络结构，预测精度更高，拟合能力更强。
Abstract: In order to solve the problem that the soluble zinc ratio is difficult to be measured online in the roasting quality of zinc smelting and roasting process, a Deep Belief Network (DBN) algorithm was proposed to predict the soluble zinc rate. However, the DBN network structure is an important factor affecting its prediction performance, and it is difficult to determine the appropriate network structure. It is proposed to use the information entropy method to determine the appropriate number of hidden layer, then use the PSO algorithm to optimize the number of hidden layer nodes and learning rate, and finally determine the DBN network structure. The method was validated by data set simulation and practical application of soluble zinc rate prediction, and compared with BP neural network and RBF neural network models. The results show that the DBN network structure optimized by the information entropy method and the PSO algorithm has higher prediction accuracy and stronger fitting ability.
文章引用：杨红川, 李勇刚. 基于粒子群算法的深度置信网络焙烧过程可溶锌率预测[J]. 计算机科学与应用, 2020, 10(1): 141-153. https://doi.org/10.12677/CSA.2020.101016

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