FORECAST OF COAL HEAT WITH ROUGH SETS THEORY AND ARTIFICIAL NEURAL NETWORKS

摘要：

To forecast the quantity of coal heat, six parameters affecting the coal heat is selected to build the decision table. And then, rough sets theory is applied to reduce the original decision table. Finally, the reduction results are transformed into rules, which are used as input of the BP neural networks to build the forecasting model. By analyzing and contrasting the example，it is confirmed that the model could not only reduce structure and training step of the neural network effectively, but also improve the learning efficiency and forecast the quantity of coal heat well.

关键词： rough sets reduction neural network coal heat

作者: Yongmao Wang Yukun Wang

作者单位: School of Computer Science and Technology,Henan Polytechnic University,Jiaozuo,Henan 454003,China

会议类型: 国际会议

会议名称: 2008年拟人系统国际会议(2008 International Conference on Humanized Systems )(ICHS’08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-18（万方平台首次上网日期，不代表论文的发表时间）