Relational Compressed Database Images (for Accelerated Querying of Databases)
    1.
    发明申请
    Relational Compressed Database Images (for Accelerated Querying of Databases) 审中-公开
    关系压缩数据库图像(用于加速查询数据库)

    公开(公告)号:US20080133573A1

    公开(公告)日:2008-06-05

    申请号:US11793802

    申请日:2005-12-19

    IPC分类号: G06F17/30

    CPC分类号: G06F16/2428 G06F16/2423

    摘要: The invention relates to a data bank interrogation system, wherein two or more data bank tables are linked by means of a common key or several keys which are respectively common to at least two data bank tables. In an analysis query and a selection of data sets in the first data bank, a selection of data sets is determined in the second data bank corresponding to the selection according to the common key and the analysis query is answered using the thus selected data sets in the second data bank.

    摘要翻译: 本发明涉及一种数据库询问系统,其中两个或多个数据库表通过公共密钥或多个密钥相连,这些密钥分别对至少两个数据库表共同。 在第一数据库中的分析查询和数据集的选择中,根据公共密钥在对应于选择的第二数据库中确定数据集的选择,并且使用这样选择的数据集来回答分析查询 第二个数据库。

    Method for determining state parameters of a chemical reactor with
artificial neural networks
    2.
    发明授权
    Method for determining state parameters of a chemical reactor with artificial neural networks 失效
    用人造神经网络确定化学反应器的状态参数的方法

    公开(公告)号:US6029157A

    公开(公告)日:2000-02-22

    申请号:US691248

    申请日:1996-08-02

    申请人: Oliver Mihatsch

    发明人: Oliver Mihatsch

    摘要: A sequence of measured quantities is determined for a chemical reactor and a data generator generates a curve in normal coordinates from a respective predetermined normal form that describes a type of critical state. Each normal form is imaged onto the sequence of measured quantities by neural networks whereby a respective neural network is allocated to a data generator. The imaging is optimized by applying parameter optimization methods. The neural network that converges best through use of parameter optimization method describes the critical state that lies closest to the actual state of the chemical reactor.

    摘要翻译: 为化学反应器确定测量量的序列,数据发生器从描述临界状态类型的各自的预定法线形成正常坐标中的曲线。 每个正常形式通过神经网络成像到测量量的序列上,由此相应的神经网络被分配给数据发生器。 通过应用参数优化方法优化成像。 通过使用参数优化方法收敛最好的神经网络描述了最接近化学反应器实际状态的临界状态。