Chaotic Identification and Prediction of Silicon Content in Hot Metal

The time series data of silicon content in hot metal were identified to have the chaotic feature because of the positive maximum Lyapunov exponent, and then the time scales to predict future were estimated. Finally a chaotic local-region model was constructed to predict silicon content in hot metal with good performance due to high hitting rate.

Identification of LPV system using locally weighted technique

The problem of linear parameter varying （LPV） system identification is considered based on the locally weighted technique which provides estimation of the LPV model parameters at each distinct data time point by giving large weights to measurements that are ＂close＂ to the current time point and small weights to measurements ＂far＂ from the current time point. Issues such as choice of distance function, weighting function and bandwidth selection are discussed. The developed method is easy to implement and simulation results illustrate its efficiency.

华罗庚管理科学与工业大数据分析的系统工程

本文简介了攀钢顾问华罗庚教授亲自指导完成的攀枝花钒钛资源综合利用中的国家重大攻关项目成果,以“国家科技进步一等奖”中的典型大数据分析案例为例,来论述华罗庚教授所创建的管理科学理论对钢铁工业大数据分析、智能优化算法和工艺智能制造上的指导作用。通过对具体案例和算法的介绍,能够帮助我们更深刻地理解华罗庚管理科学36字方针的丰富内涵。特别是在今天数字经济时代,如何应用华罗庚管理科学理论指导工业生产线智能制造技术的研发,具有普遍的指导意义。