传统数据仓库由ODS(Operational Data Stone)、数据仓库、数据集市和BI工具组成。实时数据仓库扩展了传统数据仓库的适用范围, 能给企业提供关于日常战术操作的技术支持。该文讨论了实时数据仓库的几种体系实现,并同传统数据仓库体系进...传统数据仓库由ODS(Operational Data Stone)、数据仓库、数据集市和BI工具组成。实时数据仓库扩展了传统数据仓库的适用范围, 能给企业提供关于日常战术操作的技术支持。该文讨论了实时数据仓库的几种体系实现,并同传统数据仓库体系进行了比较和分析。通过对 需求、技术、性能等方面的分析。提出了比较可行的实时数据仓库体系结构。展开更多
The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and t...The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.展开更多
文摘传统数据仓库由ODS(Operational Data Stone)、数据仓库、数据集市和BI工具组成。实时数据仓库扩展了传统数据仓库的适用范围, 能给企业提供关于日常战术操作的技术支持。该文讨论了实时数据仓库的几种体系实现,并同传统数据仓库体系进行了比较和分析。通过对 需求、技术、性能等方面的分析。提出了比较可行的实时数据仓库体系结构。
基金Projects(2007AA041401,2007AA04Z194) supported by the National High Technology Research and Development Program of China
文摘The traditional prediction methods of element yield rate can be divided into experience method and data-driven method.But in practice,the experience formulae are found to work only under some specific conditions,and the sample data that are used to establish data-driven models are always insufficient.Aiming at this problem,a combined method of genetic algorithm(GA) and adaptive neuro-fuzzy inference system(ANFIS) is proposed and applied to element yield rate prediction in ladle furnace(LF).In order to get rid of the over reliance upon data in data-driven method and act as a supplement of inadequate samples,smelting experience is integrated into prediction model as fuzzy empirical rules by using the improved ANFIS method.For facilitating the combination of fuzzy rules,feature construction method based on GA is used to reduce input dimension,and the selection operation in GA is improved to speed up the convergence rate and to avoid trapping into local optima.The experimental and practical testing results show that the proposed method is more accurate than other prediction methods.
