【摘要】 钢铁行业是我国国民经济的支柱产业之一,为国民经济的持续发展作出了积极的贡献。因此,对钢铁产量的预测研究已经成为一项非常重要的课题。文章使用基于数据挖掘和知识发现的人工神经网络法、时间序列分析法、递归神经网络技术来预测钢铁产量的方法,并将递归神经网络方法预测的结果与前面的两种方法的预测结果进行比较,比较的结果说明该方法是可行的。更多还原

【Abstract】 The iron and steel industry is one of the pillar industries of the national economy in China and makes positive contribution to the sustainable development of the national economy. So, the predicting study of iron and steel industry has been a very important research topic. This paper employs the neural network method, time series analysis method and recursive neural networks technology based on data mining and knowledge discovery to predict the iron and steel output. In this paper, the emphasis is laid on recursive neural networks technology. It makes comparison between its predicted results and those of the above two methods. The comparison result shows that recursive neural networks technology is feasible.更多还原