【作者】 刘剑；

【导师】 袁守谦；

【作者基本信息】 西安建筑科技大学 ， 钢铁冶金`， 2003， 硕士

【摘要】 模式识别和人工神经网络方法是处理数据的有用技术。特别适用于影响因素多、错综复杂的数据集，以提取有用信息。将模式识别和人工神经网络技术应用到转炉炼钢过程控制中，可在一定程度上克服常规模型的不足，提高终点命中率，为生产优化提供指导。 论文通过模式识别中的PLS(偏最小二乘法)分析，建立了转炉冶炼终点磷含量统计模型，由主成分映照图中找出提高磷分配比的参数优化方向。为了预测终点磷含量，建立了基于改进BP(前向反馈)网络算法的磷含量人工神经网络模型。调整网络参数，结合现场数据训练和预报，终点磷含量在偏差±0.002％的预测命中率达到74％。同时，利用模式识别和神经网络结合的PLS-BP降维网络设计了4个冶炼工艺参数点，预测的结果与理论和实际都比较符合。更多还原

【Abstract】 PR (Pattern Recognition) and ANN (Artificial Neural Network) are effective methods of data processing. They are especially useful to the extraction of information from complicated data set influenced by many factors. Applying PR and ANN in the converter steel-making process has the advantages, which could get over deficiencies in general models in a certain extent, improve the hit rate of the end molten steel and guide optimization in production.Through PLS(Partial Least Squares) belongs to PR, a statistical model of phosphorus distribution ratio Lp in LD converter processing was established and the optimized direction of parameters that improves Lp was confirmed from the principal components’ mapping fig. For predicting end phosphorus content, an ANN model of end phosphorus content was established based on the improved BP(Background Propagation) network arithmetic. The hit rate of end phosphorus content gets 74% when error of predicting value range is from -0.002% to 0.002% through regulating network parameters and using real-time data. Moreover, four parameters points was designed using PLS-BP method, and its results accord with theory and practice preferably.更多还原