神经网络在稀土电解中的应用研究  

The Study about the Relation between Rare-earth-melted-production's Various Factors and Output-rate

作　　者：邓左民[1] 林平[2] 王俊[1] 

机构地区：[1]赣州有色冶金研究所,江西赣州341000 [2]江西理工大学机电学院,江西赣州341000

出　　处：《江西有色金属》2007年第3期33-34,47,共3页Jiangxi Nonferrous Metals

摘　　要：以工作电流、电压、单位时间加料量为输入,以金属产出投入比为输出,以大量的生产一线数据为训练样本,研究建立了一种基于BP神经网络算法的电解槽产出率模型。研究表明,该模型不仅能够较准确地预测工作电流、电压、加料情况与产出率的关系,而且为增加电解产品的产量及提高生产效率提供了新的依据。This article has established one model of electrobath＇s output-rate based on BP neural networks by taking working current, working voltage and unit time＇ s weight of adding raw-material as networks＇ input, the ratio of output and input of the metal as networks＂ output and large amount of the working data on the spot as the training sample. The research indicates this model can not only accurately forecast the relation between various quantities and the output-rate, but also provide new argument for increasing the electrolysis product and enhancing the production efficiency.

关 键 词：稀土电解槽 产出率 神经网络 BP算法 

分 类 号：TF845[冶金工程—有色金属冶金]