Supporting method and system for process operation
    1.
    发明公开
    Supporting method and system for process operation 失效
    Unterstützungsverfahrenund -vorrichtungfürden Betrieb einer Anlage

    公开(公告)号:EP0708390A2

    公开(公告)日:1996-04-24

    申请号:EP95118768.1

    申请日:1990-03-13

    申请人: HITACHI, LTD.

    IPC分类号: G05B13/02

    摘要: A method for extracting as knowledge causal relationships between input variables and an output variable of a neural circuit model, said neural circuit model being of a hierarchical structure constructed of an input layer, at least one hidden layer and an output layer and having performed learning a limited number of times by determining weight factors between mutually-connected neuron element models in different layers of the input layer, hidden layer and output layer, wherein with respect to plural routes extending from a neuron element model, corresponding to a particular input variable, of the input layer to a neuron element model, corresponding to a particular output variable, of the output layer by way of the individual neuron element models of the hidden layer, a product of the weight factors for each of the routes is determined, and the products for the plural routes are summed, whereby the sum is employed as a measure for the determination of the causal relationship between the particular input variable and the particular output variable.

    摘要翻译: 一种用于提取输入变量与神经电路模型的输出变量之间的知识因果关系的方法,所述神经电路模型是由输入层,至少一个隐藏层和输出层构成的分层结构,并且已经执行了学习 通过确定输入层,隐层和输出层的不同层中相互连接的神经元元素模型之间的权重因子的有限次数,其中相对于从对应于特定输入变量的神经元元素模型延伸的多个路线, 通过隐藏层的各个神经元元素模型将输入层输入到输出层对应于特定输出变量的神经元元素模型,确定每个路线的权重因子的乘积,并且产品 多数路线被归纳为总和,由此作为衡量第th之间因果关系的措施 特定的输入变量和特定的输出变量。

    Supporting method and system for process operation
    6.
    发明公开
    Supporting method and system for process operation 失效
    用于操作植物支撑的方法和设备

    公开(公告)号:EP0708390A3

    公开(公告)日:1997-04-02

    申请号:EP95118768.1

    申请日:1990-03-13

    申请人: HITACHI, LTD.

    IPC分类号: G05B13/02

    摘要: A method for extracting as knowledge causal relationships between input variables and an output variable of a neural circuit model, said neural circuit model being of a hierarchical structure constructed of an input layer, at least one hidden layer and an output layer and having performed learning a limited number of times by determining weight factors between mutually-connected neuron element models in different layers of the input layer, hidden layer and output layer, wherein with respect to plural routes extending from a neuron element model, corresponding to a particular input variable, of the input layer to a neuron element model, corresponding to a particular output variable, of the output layer by way of the individual neuron element models of the hidden layer, a product of the weight factors for each of the routes is determined, and the products for the plural routes are summed, whereby the sum is employed as a measure for the determination of the causal relationship between the particular input variable and the particular output variable.