增量式遗传RBF算法在转炉提钒中的应用

Application of Incremental Genetic RBF NN in Converter Vanadium Recover

转炉提钒过程是一个非常复杂的多元非线性反应过程,从统计学和反应机理等角度出发,难以建立终点控制静态模型。为此提出了基于增量式遗传RBF算法。它不仅克服了RBF中心个数选择的随机性,较好地解决了样本聚类,而且为了保证网络结构能适应不断扩大的数据集,提出了增量数据处理方法,对原有网络参数进行修正,有利于连续生产操作。试验表明,用该算法预测冷却剂加入量,结果的误差较小,满足了终点命中率在90%以上的指标,具有工程实用性。

Converter vanadium recover is a very sophisticate reaction which is diverse and non- line. From the point of view of statistics and reaction mechanism, it is difficult to build up end- point control static model. Aim at this problem, the paper puts forward a model identify method based on incremental genetic RBF neural network to build up such a model, which can perfectly resolve the problem of random selection of RBF cluster center number and sample data clustering. Furthermore, in order to ensure structure of neural network to fit with continuous incremental data set, the paper presents a method of incremental data dealing, which is applied to amend the parameters of neural network. Then the request of continuous production is satisfied. Finally the result of test shows that after adopting the algorithm, the error of result is less than before and end- point hitting ratio satisfies to ninety percent. These indicate the algorithm has the engineering practicability.