Application of Variable-metric Chaos Optimization Neural Network in Predicting Slab Surface Temperature of the Continuous Casting

摘要：

A slab surface temperature prediction model of the continuous casting based on the variable-metric chaos optimization neural network is presented to solve the problem which the slab surface temperatures can not be measured continuously directly for plentiful inhalator, water film and ferric oxide on the slab surface in the secondary cooling zone. The model is shown to fit the actual data precisely and to overcome several disadvantages of the conventional BP neural networks, namely: slow convergence, low accuracy and difficulty in finding the global optimum. A series of tests have been conducted based on the inputs of the continuous casting in a steel factory. It has been shown that the error is less than 1％ between the predicted surface temperatures with the model and the actual temperatures, and the error is less than 2％ between the predicted slab thicknesses with the model and the actual slab thicknesses. The model has yielded highly desirable results.

关键词： variable-metric chaos optimization neural network slab surface temperature prediction

作者: Fengxiang Gao Changsong Wang Yubao Zhang Xiao Chen

作者单位: Mechatronic Engineering Department, University of Science and Technology Beijing, Beijing 100083, Ch Mechatronic Engineering Department, University of Science and Technology Beijing, Beijing 100083, Ch Anyang Iron and Steel Company, Anyang 455004, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 2296-2299

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）