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    • 4. 发明公开
    • Control system using an adaptive neural network for target and path optimization for a multivariable, nonlinear process
    • 使用一个自适应的神经网络,以优化以更尺寸/非线性系统的目标和路径选择控制系统。
    • EP0588594A2
    • 1994-03-23
    • EP93307225.8
    • 1993-09-14
    • TEXACO, Inc.NEURALWARE, Inc.
    • Graettinger, Timothy J.Dubose, Paul A.Federowicz, Alexander J.Bhat, Naveen V.Braden, William B.Heckendoorn, Kent E.
    • G05B13/02B01D3/42
    • G05B13/027B01D3/425B01J19/0033Y10S706/903Y10S706/906
    • A control system having four major components: a target optimizer, a path optimizer, a neural network adaptation controller and a neural network. In the target optimizer, the controlled variables are optimized to provide the most economically desirable outputs, subject to operating constraints. Various manipulated variable and disturbance values are provided for modeling purposes. The neural network receives as inputs a plurality of settings for each manipulated and disturbance variable. For target optimization all the neural network input values are set equal to produce a steady state controlled variable value. The entire process is repeated with differing manipulated variable values until an optimal solution develops. The resulting target controlled and manipulated variable values are provided to the path optimizer to allow the manipulated variables to be adjusted to obtain the target output. Various manipulated variable values are developed to model moves from current to desired values. In this case trend indicating values of the manipulated and disturbance variables are provided to produce time varying values of the controlled variables. The process is repeated until an optimal path is obtained, at which time the manipulated variable values are applied to the actual process. On a periodic basis all of the disturbance, manipulated and controlled variables are sampled to find areas where the training of the neural network is sparse or where high dynamic conditions are indicated. These values are added to the set of values used to train the neural network.
    • 目标优化器,一个路径优化器,神经网络适配控制器和神经网络:具有四个主要组成部分的控制系统。 在目标优化器,受控变量进行优化,以提供最经济上所需的输出,受操作约束。 被提供用于建模的目的的各种操纵变量和干扰值。 神经网络接收输入作为为每个操纵和扰动变量设置多个的情况。 对于目标优化所有的神经网络的输入值被设置等于以产生一个稳定状态控制变量的值。 整个过程重复具有不同操纵变量的值,直到在最优解演变。 将所得的目标控制并且被提供给路径优化器操纵变量的值,以允许操作量进行调整,以获得目标输出。 各种操纵变量值的开发,以模拟从当前到期望的值移动。 在这种情况下趋势指示操纵和扰动变量的值被提供,以产生随时间变化的受控变量的值。 重复该过程,直到在最佳路径获得,在WhichTime操纵变量的值被施加到的实际过程。 在定期的所有干扰,操纵和控制变量抽样找到所在区域的神经网络训练是稀疏或高动态条件。另外。 这些值被添加到该组用于训练神经网络的值。