化工学报2016，Vol.67Issue(3)：897-902,6.DOI:10.11949/j.issn.0438-1157.20151940
动态RBF神经网络在浮选过程模型失配中的应用
Dynamic RBF neural networks for model mismatch problem and its application in flotation process
摘要
Abstract
It is difficult to measure the process parameters online in the bauxite froth flotation process because the slurry deposits quickly. Especially, frequent change of the characteristics of the ore makes the process parameters change from time to time. So that, the static soft sensing models, such as the neural network model, which was obtained by a fixed set of training samples, may not track the dynamic characteristics of the process caused by change of the ore resource. And, thus, model mismatch problem occurs. In this paper, for model mismatch problem under various ore sources, dynamic RBF neural network modeling method based on the hidden layer node dynamic allocation and model parameters dynamic correction strategy is proposed. And the model is used for online measurement of the pH of the slurry in the flotation process, simulation results show that the dynamic model can solve the model mismatch problem well.关键词
泡沫浮选过程/动态RBF神经网络/模型失配/工况迁移Key words
froth flotation process/dynamic RBF neural network/model mismatch/migration of working condition分类
化学化工引用本文复制引用
王晓丽,黄蕾,杨鹏,阳春华..动态RBF神经网络在浮选过程模型失配中的应用[J].化工学报,2016,67(3):897-902,6.基金项目
国家自然科学基金项目（61304126，61473318，61134006，61304019）。@@@@supported by the National Natural Science Foundation of China (61304126,61473318,61134006,61304019) （61304126,61473318,61134006,61304019）
