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1. 发明授权

US07058617B1 Method and apparatus for training a system model with gain constraints 有权
公开(公告)号：US07058617B1
公开(公告)日：2006-06-06
申请号：US09662243
申请日：2000-09-14
申请人： Eric Jon Hartman , Stephen Piche , Mark Gerules
发明人： Eric Jon Hartman , Stephen Piche , Mark Gerules
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00
CPC分类号： G06N3/02 , G05B13/027 , G05B13/042 , G05B13/048 , G05B17/02
摘要： Method and apparatus for training a system model with gain constraints. A method is disclosed for training a steady-state model, the model having an input and an output and a mapping layer for mapping the input to the output through a stored representation of a system. A training data set is provided having a set of input data u(t) and target output data y(t) representative of the operation of a system. The model is trained with a predetermined training algorithm which is constrained to maintain the sensitivity of the output with respect to the input substantially within user defined constraint bounds by iteratively minimizing an objective function as a function of a data objective and a constraint objective. The data objective has a data fitting learning rate and the constraint objective has constraint learning rate that are varied as a function of the values of the data objective and the constraint objective after selective iterative steps.

2. 发明授权

US06169980A Method for operating a neural network with missing and/or incomplete data 有权
标题翻译：用于操作具有丢失和/或不完整数据的神经网络的方法
公开(公告)号：US06169980A
公开(公告)日：2001-01-02
申请号：US09167400
申请日：1998-10-06
申请人： James David Keeler , Eric Jon Hartman , Ralph Bruce Ferguson
发明人： James David Keeler , Eric Jon Hartman , Ralph Bruce Ferguson
IPC分类号： G06F1518
CPC分类号： G06N3/0472 , G06F17/17 , G06N3/049
摘要： A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22). Additionally, a validity model (16) is also provided which represents the reliability or validity of the output as a function of the number of data points in a given data region during training of the system model (12). This predicts the confidence in the predicted output which is also input to the decision processor (20). The decision processor (20) therefore bases its decision on the predicted confidence and the predicted uncertainty. Additionally, the uncertainty output by the data preprocess block (10) can be utilized to train the system model (12).
摘要翻译：提供了一种神经网络系统，其在系统模型（12）中对系统进行建模，其输出提供预测输出。该预测输出由输出控制（14）修改或控制。在数据预处理步骤（10）中处理输入数据，以便调整用于输入到系统模型（12）的数据。另外，由和解产生的误差被输入到不确定性模型中，以预测预测输出的不确定性。这被输入到用于控制输出控制（14）的决策处理器（20）。控制输出控制器（14），以便在不确定性模型（18）的输出超过由判定阈值块（22）输入的预定判定阈值时改变预测输出或禁止预测输出。此外，还提供了有效性模型（16），其表示在系统模型（12）的训练期间作为给定数据区域中的数据点的数量的函数的输出的可靠性或有效性。这预测了也输入到决策处理器（20）的预测输出的置信度。因此，决策处理器（20）将其决定基于预测的置信度和预测的不确定性。此外，可以利用数据预处理块（10）输出的不确定性来训练系统模型（12）。

3. 发明授权

US06725208B1 Bayesian neural networks for optimization and control 有权
标题翻译：贝叶斯神经网络优化与控制
公开(公告)号：US06725208B1
公开(公告)日：2004-04-20
申请号：US09290791
申请日：1999-04-12
申请人： Eric Jon Hartman , Carsten Peterson , Stephen Piche
发明人： Eric Jon Hartman , Carsten Peterson , Stephen Piche
IPC分类号： G06F1518
CPC分类号： G05B13/027 , Y10S706/906 , Y10S706/914
摘要： An optimization system is provided utilizing a Bayesian neural network calculation of a derivative wherein an output is optimized with respect to an input utilizing a stochastical method that averages over many regression models. This is done such that constraints from first principal models are incorporated in terms of prior art distributions.
摘要翻译：利用贝叶斯神经网络计算一个导数来提供一个优化系统，其中输出利用利用许多回归模型平均的随机方法相对于输入进行优化。这样做是为了使得根据现有技术分布来引入来自第一主模型的约束。

4. 发明授权

US06591254B1 Method and apparatus for operating a neural network with missing and/or incomplete data 失效
标题翻译：用于操作具有缺失和/或不完整数据的神经网络的方法和装置
公开(公告)号：US06591254B1
公开(公告)日：2003-07-08
申请号：US10040085
申请日：2001-11-06
申请人： James David Keeler , Eric Jon Hartman , Ralph Bruce Ferguson
发明人： James David Keeler , Eric Jon Hartman , Ralph Bruce Ferguson
IPC分类号： G06F1518
CPC分类号： G06N3/049 , G06N3/0472
摘要： A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22). Additionally, a validity model (16) is also provided which represents the reliability or validity of the output as a function of the number of data points in a given data region during training of the system model (12). This predicts the confidence in the predicted output which is also input to the decision processor (20). The decision processor (20) therefore bases its decision on the predicted confidence and the predicted uncertainty. Additionally, the uncertainty output by the data preprocess block (10) can be utilized to train the system model (12).
摘要翻译：提供了一种神经网络系统，其在系统模型（12）中对系统进行建模，其输出提供预测输出。该预测输出由输出控制（14）修改或控制。在数据预处理步骤（10）中处理输入数据，以便调整用于输入到系统模型（12）的数据。另外，由和解产生的误差被输入到不确定性模型中，以预测预测输出的不确定性。这被输入到用于控制输出控制（14）的决策处理器（20）。控制输出控制器（14），以便在不确定性模型（18）的输出超过由判定阈值块（22）输入的预定判定阈值时改变预测输出或禁止预测输出。此外，还提供了有效性模型（16），其表示在系统模型（12）的训练期间作为给定数据区域中的数据点的数量的函数的输出的可靠性或有效性。这预测了也输入到决策处理器（20）的预测输出的置信度。因此，决策处理器（20）将其决定基于预测的置信度和预测的不确定性。此外，可以利用数据预处理块（10）输出的不确定性来训练系统模型（12）。

5. 发明授权

US06363289B1 Residual activation neural network 失效
标题翻译：残余激活神经网络
公开(公告)号：US06363289B1
公开(公告)日：2002-03-26
申请号：US09228962
申请日：1999-01-12
申请人： James David Keeler , Eric Jon Hartman , Kadir Liano , Ralph Bruce Ferguson
发明人： James David Keeler , Eric Jon Hartman , Kadir Liano , Ralph Bruce Ferguson
IPC分类号： G05B1302
CPC分类号： G05B13/027
摘要： A plant (72) is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) is provided that accurately models the plant (72). The output of the control network (74) provides a predicted output which is combined with a desired output to generate an error. This error is back propagated through an inverse control network (76), which is the inverse of the control network (74) to generate a control error signal that is input to a distributed control system (73) to vary the control inputs to the plant (72) in order to change the output y(t) to meet the desired output. The control network (74) is comprised of a first network NET 1 that is operable to store a representation of the dependency of the control variables on the state variables. The predicted result is subtracted from the actual state variable input and stored as a residual in a residual layer (102). The output of the residual layer (102) is input to a hidden layer (108) which also receives the control inputs to generate a predicted output in an output layer (106). During back propagation of error, the residual values in the residual layer (102) are latched and only the control inputs allowed to vary.
摘要翻译：工厂（72）可操作以接收控制输入c（t）并提供输出y（t）。工厂（72）具有与其不可变的状态变量s（t）相关联。提供控制网络（74），其精确地模拟设备（72）。控制网络（74）的输出提供与期望输出组合以产生错误的预测输出。该错误通过逆控制网络（76）反向传播，逆控制网络（76）是控制网络（74）的反向，以产生输入到分布式控制系统（73）的控制误差信号，以改变对工厂的控制输入（72），以便改变输出y（t）以满足期望的输出。控制网络（74）包括第一网络NET1，其可操作以存储控制变量对状态变量的依赖性的表示。将预测结果从实际状态变量输入中减去并作为剩余层存储在残留层（102）中。剩余层（102）的输出被输入到隐藏层（108），隐层也接收控制输入以在输出层（106）中生成预测输出。在误差的反向传播期间，残留层（102）中的残余值被锁存，并且仅允许控制输入变化。

6. 发明授权

US06314414B1 Method for training and/or testing a neural network with missing and/or incomplete data 失效
标题翻译：用于训练和/或测试具有缺失和/或不完整数据的神经网络的方法
公开(公告)号：US06314414B1
公开(公告)日：2001-11-06
申请号：US09207719
申请日：1998-12-08
申请人： James David Keeler , Eric Jon Hartman , Ralph Bruce Ferguson
发明人： James David Keeler , Eric Jon Hartman , Ralph Bruce Ferguson
IPC分类号： G06F1518
CPC分类号： G06N3/049 , G06N3/0472
摘要： A neural network system is provided that models the system in a system model (12) with the output thereof providing a predicted output. This predicted output is modified or controlled by an output control (14). Input data is processed in a data preprocess step (10) to reconcile the data for input to the system model (12). Additionally, the error resulted from the reconciliation is input to an uncertainty model to predict the uncertainty in the predicted output. This is input to a decision processor (20) which is utilized to control the output control (14). The output control (14) is controlled to either vary the predicted output or to inhibit the predicted output whenever the output of the uncertainty model (18) exceeds a predetermined decision threshold, input by a decision threshold block (22). Additionally, a validity model (16) is also provided which represents the reliability or validity of the output as a function of the number of data points in a given data region during training of the system model (12). This predicts the confidence in the predicted output which is also input to the decision processor (20). The decision processor (20) therefore bases its decision on the predicted confidence and the predicted uncertainty. Additionally, the uncertainty output by the data preprocess block (10) can be utilized to train the system model (12).
摘要翻译：提供了一种神经网络系统，其在系统模型（12）中对系统进行建模，其输出提供预测输出。该预测输出由输出控制（14）修改或控制。在数据预处理步骤（10）中处理输入数据，以便调整用于输入到系统模型（12）的数据。另外，由和解产生的误差被输入到不确定性模型中，以预测预测输出的不确定性。这被输入到用于控制输出控制（14）的决策处理器（20）。控制输出控制器（14），以便在不确定性模型（18）的输出超过由判定阈值块（22）输入的预定判定阈值时改变预测输出或禁止预测输出。此外，还提供了有效性模型（16），其表示在系统模型（12）的训练期间作为给定数据区域中的数据点的数量的函数的输出的可靠性或有效性。这预测了也输入到决策处理器（20）的预测输出的置信度。因此，决策处理器（20）将其决定基于预测的置信度和预测的不确定性。此外，可以利用数据预处理块（10）输出的不确定性来训练系统模型（12）。

7. 发明授权

US06216048B1 Method and apparatus for determining the sensitivity of inputs to a neural network on output parameters 有权
标题翻译：用于确定输入参数对神经网络的输入灵敏度的方法和装置
公开(公告)号：US06216048B1
公开(公告)日：2001-04-10
申请号：US09174860
申请日：1998-10-19
申请人： James David Keeler , Eric Jon Hartman , Kadir Liano
发明人： James David Keeler , Eric Jon Hartman , Kadir Liano
IPC分类号： G05B1302
CPC分类号： G05B13/027 , G05B13/0285 , G06N3/04 , G06N3/08
摘要： A distributed control system (14) receives on the input thereof the control inputs and then outputs control signals to a plant (10) for the operation thereof. The measured variables of the plant and the control inputs are input to a predictive model (34) that operates in conjunction with an inverse model (36) to generate predicted control inputs. The predicted control inputs are processed through a filter (46) to apply hard constraints and sensitivity modifiers, the values of which are received from a control parameter block (22). During operation, the sensitivity of output variables on various input variables is determined. This information can be displayed and then the user allowed to select which of the input variables constitute the most sensitive input variables. These can then be utilized with a control network (470) to modify the predicted values of the input variables. Additionally, a neural network (406) can be trained on only the selected input variables that are determined to be the most sensitive. In this operation, the network is first configured and trained with all input nodes and with all training data. This provides a learned representation of the output wherein the combined effects of all other input variables are taken into account in the determination of the effect of each of the input variables thereon. The network (406) is then reconfigured with only the selected inputs and then the network (406) again trained on only the input/output pairs associated with the select input variables.
摘要翻译：分布式控制系统（14）在其输入端接收控制输入，然后将控制信号输出到用于其操作的设备（10）。工厂和控制输入的测量变量被输入到与逆模型（36）一起工作以产生预测控制输入的预测模型（34）。通过滤波器（46）处理预测的控制输入以施加硬约束和灵敏度修正器，其值从控制参数块（22）接收。在运行期间，确定输出变量对各种输入变量的敏感度。可以显示该信息，然后用户可以选择哪个输入变量构成最敏感的输入变量。然后可以利用这些控制网络（470）来修改输入变量的预测值。另外，神经网络（406）可以仅对被确定为最敏感的所选择的输入变量进行训练。在此操作中，网络首先用所有输入节点和所有训练数据进行配置和训练。这提供了输出的学习表示，其中在确定其中每个输入变量的影响时考虑所有其他输入变量的组合效应。然后，仅使用所选择的输入来重新配置网络（406），然后再次仅对与选择输入变量相关联的输入/输出对训练网络（406）。

8. 发明授权

US07315846B2 Method and apparatus for optimizing a system model with gain constraints using a non-linear programming optimizer 有权
标题翻译：使用非线性规划优化器优化具有增益约束的系统模型的方法和装置
公开(公告)号：US07315846B2
公开(公告)日：2008-01-01
申请号：US11396868
申请日：2006-04-03
申请人： Eric Jon Hartman , Stephen Piche , Mark Gerules
发明人： Eric Jon Hartman , Stephen Piche , Mark Gerules
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00
CPC分类号： G06N3/02 , G05B13/027 , G05B13/042 , G05B13/048 , G05B17/02
摘要： Method and apparatus for training a system model with gain constraints. A method is disclosed for training a steady-state model, the model having an input and an output and a mapping layer for mapping the input to the output through a stored representation of a system. A training data set is provided having a set of input data u(t) and target output data y(t) representative of the operation of a system. The model is trained with a predetermined training algorithm which is constrained to maintain the sensitivity of the output with respect to the input substantially within user defined constraint bounds by iteratively minimizing an objective function as a function of a data objective and a constraint objective. The data objective has a data fitting learning rate and the constraint objective has constraint learning rate that are varied as a function of the values of the data objective and the constraint objective after selective iterative steps.
摘要翻译：用于训练具有增益约束的系统模型的方法和装置。公开了一种用于训练稳态模型的方法，该模型具有输入和输出以及用于通过存储的系统表示将输入映射到输出的映射层。提供具有代表系统的操作的一组输入数据u（t）和目标输出数据y（t）的训练数据集。用预定的训练算法来训练该模型，该训练算法被约束以通过将作为数据目标和约束目标的函数的目标函数迭代地最小化来维持相对于基本上在用户定义的约束边界内的输入的输出的灵敏度。数据目标具有数据拟合学习率，并且约束目标具有约束学习速率，其作为数据目标的值和选择性迭代步骤之后的约束目标的函数而变化。

9. 发明授权

US07213006B2 Method and apparatus for training a system model including an integrated sigmoid function 有权
标题翻译：用于训练包括综合S型功能的系统模型的方法和装置
公开(公告)号：US07213006B2
公开(公告)日：2007-05-01
申请号：US11267812
申请日：2005-11-04
申请人： Eric Jon Hartman , Stephen Piche , Mark Gerules
发明人： Eric Jon Hartman , Stephen Piche , Mark Gerules
IPC分类号： G06E1/00 , G06E3/00 , G06F15/18 , G06G7/00
CPC分类号： G06N3/02 , G05B13/027 , G05B13/042 , G05B13/048 , G05B17/02
摘要： Method and apparatus for training a system model with gain constraints. A method is disclosed for training a steady-state model having an input and an output and a mapping layer for mapping the input to the output, the model comprising a stored representation of a plant or process, and including a linear portion and a non-linear portion, where the non-linear portion includes a function. Input is received to the model, and predicted output computed corresponding to attribute(s) of the plant or process. The predicted output is stored, and is usable to manage the plant or process. The model is trained to optimize a specified objective function subject to one or more constraints, e.g., via a non-linear programming (NLP) optimizer, the constraints including, hard constraint(s) comprising strict limitations on the training in optimizing the objective function, and/or soft constraint(s) comprising a weighted penalty function included in the objective function.
摘要翻译：用于训练具有增益约束的系统模型的方法和装置。公开了一种用于训练具有输入和输出的稳态模型的方法，以及用于将输入映射到输出的映射层，该模型包括存储的工厂或过程的表示，并且包括线性部分和非线性部分，线性部分，其中非线性部分包括功能。输入被接收到模型，并且根据工厂或过程的属性计算出的预测输出。存储预测输出，并可用于管理工厂或过程。训练该模型以优化受一个或多个约束的指定目标函数，例如，通过非线性规划（NLP）优化器，约束包括硬约束（包括对优化目标函数的训练的严格限制），和/或包括目标函数中包括的加权惩罚函数的软约束。

10. 发明授权

US06985781B2 Residual activation neural network 失效
标题翻译：残余激活神经网络
公开(公告)号：US06985781B2
公开(公告)日：2006-01-10
申请号：US10041157
申请日：2002-01-08
申请人： James David Keeler , Eric Jon Hartman , Kadir Liano , Ralph Bruce Ferguson
发明人： James David Keeler , Eric Jon Hartman , Kadir Liano , Ralph Bruce Ferguson
IPC分类号： G05B13/02
CPC分类号： G05B13/027
摘要： A plant is operable to receive control inputs c(t) and provide an output y(t). The plant (72) has associated therewith state variables s(t) that are not variable. A control network (74) models the plant by providing a predicted output which is combined with a desired output to generate an error that is back propagated through an inverse control network to generate a control error signal that is input to a distributed control system to vary the control inputs to the plant in order to change the output y(t) to meet the desired output. The inverse model represents the dependencies of the plant output on the control variables parameterized by external influences to the plant.
摘要翻译：工厂可操作以接收控制输入c（t）并提供输出y（t）。工厂（72）具有与其不可变的状态变量s（t）相关联。控制网络（74）通过提供与期望输出组合的预测输出来对工厂进行建模，以产生通过反向控制网络反向传播的错误，以产生输入到分布式控制系统以改变的控制误差信号控制输入到工厂，以便改变输出y（t）以满足期望的输出。逆模型表示植物输出对通过对植物的外部影响参数化的控制变量的依赖性。

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