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1. 发明授权

US5859773A Residual activation neural network 失效
公开(公告)号：US5859773A
公开(公告)日：1999-01-12
申请号：US717719
申请日：1996-09-23
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： G06F15/18 , G05B13/02 , G05B13/04 , G06N3/00 , G06F19/00
CPC分类号： G05B13/027 , Y10S706/906
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.

2. 发明授权

US5842189A Method for operating a neural network with missing and/or incomplete data 失效
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：US5842189A
公开(公告)日：1998-11-24
申请号：US936679
申请日：1997-09-27
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： G06F15/18 , G06F17/17 , G06N3/04 , G06N3/08
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

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）。

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

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。

8. 发明授权

US06957203B2 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：US06957203B2
公开(公告)日：2005-10-18
申请号：US10614335
申请日：2003-07-07
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： G06N3/04 , G06F15/18
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）。

9. 发明授权

US5819006A Method for operating a neural network with missing and/or incomplete data 失效
公开(公告)号：US5819006A
公开(公告)日：1998-10-06
申请号：US724377
申请日：1996-10-01
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： G06F15/18 , G06F17/17 , G06N3/04 , G06N3/08
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).

10. 发明授权

US08156035B2 Modeling financial instruments using bid and ask prices 失效
公开(公告)号：US08156035B2
公开(公告)日：2012-04-10
申请号：US12422615
申请日：2009-04-13
申请人： Ralph Bruce Ferguson , Liam Cheung , Ronald Scott Boyd
发明人： Ralph Bruce Ferguson , Liam Cheung , Ronald Scott Boyd
IPC分类号： G06Q40/00
CPC分类号： G06Q40/06 , G06Q20/10 , G06Q40/00 , G06Q40/04
摘要： A method for modeling an investment significant parameter of a financial instrument, using a computer. At least one series of historical bid prices of the financial instrument or historical ask prices of the financial instrument is provided. A financial model type that has at least one variable parameter is selected. The variable parameter(s) of the selected financial model type is initialized. The series of historical bid prices and/or historical ask prices is applied to the initialized financial model type to estimate the variable parameter(s). The resulting model of the financial instrument may be used to predict future values of the investment significant parameter of the financial instrument. These predicted future values may be used to determine whether to perform automated trades of the financial instrument.

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