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1. 发明授权

US5477444A Control system using an adaptive neural network for target and path optimization for a multivariable, nonlinear process 失效
标题翻译：控制系统使用自适应神经网络进行多变量非线性过程的目标和路径优化
公开(公告)号：US5477444A
公开(公告)日：1995-12-19
申请号：US944645
申请日：1992-09-14
申请人： Naveen V. Bhat , William B. Braden , Kent E. Heckendoorn , Timothy J. Graettinger , Alexander J. Federowicz , Paul A. DuBose
发明人： Naveen V. Bhat , William B. Braden , Kent E. Heckendoorn , Timothy J. Graettinger , Alexander J. Federowicz , Paul A. DuBose
IPC分类号： C10G7/00 , B01D3/42 , B01J19/00 , C10G7/12 , G05B11/32 , G05B13/02 , G05B13/04 , G05B19/02 , G06F15/18 , G06N3/00 , G06F15/46
CPC分类号： G05B13/027 , B01D3/425 , B01J19/0033 , Y10S706/903 , Y10S706/906
摘要： 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.
摘要翻译：具有四个主要部分的控制系统：目标优化器，路径优化器，神经网络适配控制器和神经网络。在目标优化器中，受控变量进行了优化，以提供最经济可取的输出，受操作限制。提供各种操纵变量和干扰值用于建模目的。神经网络接收每个被操纵和干扰变量的多个设置作为输入。对于目标优化，所有神经网络输入值都被设置为等于产生稳态受控变量值。使用不同的操纵变量值重复整个过程，直到发展出最佳解。所产生的目标控制和操纵的变量值被提供给路径优化器，以允许调整操纵的变量以获得目标输出。开发了各种操纵变量值，以模拟从当前值到期望值的移动。在这种情况下，提供了指示受控和干扰变量值的趋势，以产生受控变量的时变值。重复该过程直到获得最佳路径，此时将操纵的变量值应用于实际过程。周期性地对所有扰动，操纵和控制变量进行采样，以找到神经网络的训练稀疏或指示高动态条件的区域。这些值被添加到用于训练神经网络的值集合中。

2. 发明授权

US5640491A Control system using an adaptive neural network for target and path optimization for a multivariable, nonlinear process 失效
标题翻译：控制系统使用自适应神经网络进行多变量非线性过程的目标和路径优化
公开(公告)号：US5640491A
公开(公告)日：1997-06-17
申请号：US574250
申请日：1995-12-18
申请人： Naveen V. Bhat , William B. Braden , Kent E. Heckendoorn , Timothy J. Graettinger , Alexander J. Federowicz , Paul A. Dubose
发明人： Naveen V. Bhat , William B. Braden , Kent E. Heckendoorn , Timothy J. Graettinger , Alexander J. Federowicz , Paul A. Dubose
IPC分类号： C10G7/00 , B01D3/42 , B01J19/00 , C10G7/12 , G05B11/32 , G05B13/02 , G05B13/04 , G05B19/02 , G06F15/18 , G06N3/00
CPC分类号： G05B13/027 , B01D3/425 , B01J19/0033 , Y10S706/903 , Y10S706/906
摘要： 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.
摘要翻译：具有四个主要部分的控制系统：目标优化器，路径优化器，神经网络适配控制器和神经网络。在目标优化器中，受控变量进行了优化，以提供最经济可取的输出，受操作限制。提供各种操纵变量和干扰值用于建模目的。神经网络接收每个被操纵和干扰变量的多个设置作为输入。对于目标优化，所有神经网络输入值都被设置为等于产生稳态受控变量值。使用不同的操纵变量值重复整个过程，直到发展出最佳解。所产生的目标控制和操纵的变量值被提供给路径优化器，以允许调整操纵的变量以获得目标输出。开发了各种操纵变量值，以模拟从当前值到期望值的移动。在这种情况下，提供了指示受控和干扰变量值的趋势，以产生受控变量的时变值。重复该过程直到获得最佳路径，此时将操纵的变量值应用于实际过程。周期性地对所有扰动，操纵和控制变量进行采样，以找到神经网络的训练稀疏或指示高动态条件的区域。这些值被添加到用于训练神经网络的值集合中。

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