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    • 3. 发明申请
    • Statistical models for improving the performance of database operations
    • 提高数据库操作性能的统计模型
    • US20070083343A1
    • 2007-04-12
    • US11581452
    • 2006-10-17
    • Michael HaftReimar Hofmann
    • Michael HaftReimar Hofmann
    • G06F19/00
    • G06F16/30G06F16/2465
    • A method for performing an automatic software-driven statistical evaluation of a large amount of data to be assigned to statistical variables in a database contained in at least one cluster. The method is characterized by using a statistical model to model an approximate description of a relative frequency of the state or states of the statistical variables and a statistical dependencies between the state or states, and then determining the approximate relative frequency of the state or states of the statistical variables and the approximate relative frequency belonging to a predetermined relative frequency of the state or states of the statistical variables and an expected value of the state or states of the statistical variables dependent thereon.
    • 一种用于对包含在至少一个集群中的数据库中的统计变量分配的大量数据进行自动软件驱动统计评估的方法。 该方法的特征在于使用统计模型来模拟统计变量的状态或状态的相对频率的近似描述以及状态或状态之间的统计依赖性,然后确定状态或状态的近似相对频率 统计变量和属于统计变量的状态或状态的预定相对频率的估计相对频率以及依赖于其的统计变量的状态或状态的期望值。
    • 7. 发明授权
    • Statistical models for improving the performance of database operations
    • 提高数据库操作性能的统计模型
    • US07149649B2
    • 2006-12-12
    • US10479991
    • 2002-05-15
    • Michael HaftReimar Hofmann
    • Michael HaftReimar Hofmann
    • G06F19/00G06F15/00
    • G06F17/30539G06F17/3061
    • A method for performing an automatic software-driven statistical evaluation of a large amount of data to be assigned to statistical variables in a database contained in at least one cluster. The method is characterized by using a statistical model to model an approximate description of a relative frequency of the state or states of the statistical variables and a statistical dependencies between the state or states, and then determining the approximate relative frequency of the state or states of the statistical variables and the approximate relative frequency belonging to a predetermined relative frequency of the state or states of the statistical variables and an expected value of the state or states of the statistical variables dependent thereon.
    • 一种用于对包含在至少一个集群中的数据库中的统计变量分配的大量数据进行自动软件驱动统计评估的方法。 该方法的特征在于使用统计模型来模拟统计变量的状态或状态的相对频率的近似描述以及状态或状态之间的统计依赖性,然后确定状态或状态的近似相对频率 统计变量和属于统计变量的状态或状态的预定相对频率的估计相对频率以及依赖于其的统计变量的状态或状态的期望值。
    • 10. 发明授权
    • Method for training a neural network with the non-deterministic behavior
of a technical system
    • 用技术系统的非确定性行为训练神经网络的方法
    • US5806053A
    • 1998-09-08
    • US705834
    • 1996-08-30
    • Volker TrespReimar Hofmann
    • Volker TrespReimar Hofmann
    • G06F15/18G05B13/02G06N3/04G06N3/08
    • G05B13/027G06N3/0472G06N3/08
    • In a method for tranining a neural network with the non-deterministic behavior of a technical system, weightings for the neurons of the neural network are set during the training using a cost function. The cost function evaluates a beneficial system behavior of the technical system to be modeled, and thereby intensifies or increases the weighting settings which contribute to the beneficial system behavior, and attenuates or minimizes weightings which produce a non-beneficial behavior. Arbitrary or random disturbances are generated by disturbing the manipulated variable with noise having a known noise distribution, these random disturbances significantly faciliating the mathematical processing of the weightings which are set, because the terms required for that purpose are simplified. The correct weighting setting for the neural network is thus found on the basis of a statistical method and the application of a cost function to the values emitted by the technical system or its model.
    • 在用技术系统的非确定性行为来转移神经网络的方法中,使用成本函数在训练期间设置神经网络的神经元的加权。 成本函数评估要建模的技术系统的有益系统行为,从而加强或增加有助于有益系统行为的加权设置,并且减弱或最小化产生非有益行为的权重。 通过用具有已知噪声分布的噪声扰乱操纵变量来产生任意或随机扰动,这些随机扰动显着地促进了设定的权重的数学处理,因为简化了该目的所需的术语。 因此,基于统计方法和成本函数对技术系统或其模型发出的值的应用,可以找到神经网络的正确加权设置。