公开(公告)号：US08019701B2

公开(公告)日：2011-09-13

申请号：US12112750

申请日：2008-04-30

申请人： Bijan Sayyar-Rodsari ,  Edward Plumer ,  Eric Hartman ,  Kadir Liano ,  Celso Axelrud

发明人： Bijan Sayyar-Rodsari ,  Edward Plumer ,  Eric Hartman ,  Kadir Liano ,  Celso Axelrud

IPC分类号： G06N5/00

CPC分类号： G05B13/048 ,  G05B13/042 ,  G05B17/02

摘要： System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.

摘要翻译： 用于建模非线性过程的系统和方法。 用于非线性过程的预测优化或控制的组合模型包括耦合到参数化动态或静态模型的非线性近似器，可操作以对非线性过程建模。 非线性近似器接收过程输入，并为参数化动态模型生成参数。 参数化动态模型接收参数和过程输入，并根据参数和过程输入生成预测过程输出，其中预测过程输出可用于分析和/或控制非线性过程。 组合模型可以通过识别过程输入和输出（I / O），收集过程I / O的数据，确定来自先验知识的模型行为的约束，制定优化问题，以基本上同时的方式进行训练， 执行优化算法以确定受限于确定的模型参数，并验证模型与约束的一致性。

公开(公告)号：US20080208778A1

公开(公告)日：2008-08-28

申请号：US12112847

申请日：2008-04-30

申请人： Bijan Sayyar-Rodsari ,  Edward Plumer ,  Eric Hartman ,  Kadir Liano ,  Celson Axelrud

发明人： Bijan Sayyar-Rodsari ,  Edward Plumer ,  Eric Hartman ,  Kadir Liano ,  Celson Axelrud

IPC分类号： G06F15/18 ,  G05B13/02

CPC分类号： G05B13/048 ,  G05B13/042 ,  G05B17/02

摘要： System and method for modeling a nonlinear process. A combined model for predictive optimization or control of a nonlinear process includes a nonlinear approximator, coupled to a parameterized dynamic or static model, operable to model the nonlinear process. The nonlinear approximator receives process inputs, and generates parameters for the parameterized dynamic model. The parameterized dynamic model receives the parameters and process inputs, and generates predicted process outputs based on the parameters and process inputs, where the predicted process outputs are useable to analyze and/or control the nonlinear process. The combined model may be trained in an integrated manner, e.g., substantially concurrently, by identifying process inputs and outputs (I/O), collecting data for process I/O, determining constraints on model behavior from prior knowledge, formulating an optimization problem, executing an optimization algorithm to determine model parameters subject to the determined constraints, and verifying the compliance of the model with the constraints.

摘要翻译： 用于建模非线性过程的系统和方法。 用于非线性过程的预测优化或控制的组合模型包括耦合到参数化动态或静态模型的非线性近似器，可操作以对非线性过程建模。 非线性近似器接收过程输入，并为参数化动态模型生成参数。 参数化动态模型接收参数和过程输入，并根据参数和过程输入生成预测过程输出，其中预测过程输出可用于分析和/或控制非线性过程。 组合模型可以通过识别过程输入和输出（I / O），收集过程I / O的数据，确定来自先验知识的模型行为的约束，制定优化问题，以基本上同时的方式进行训练， 执行优化算法以确定受限于确定的模型参数，并验证模型与约束的一致性。

公开(公告)号：US20080036296A1

公开(公告)日：2008-02-14

申请号：US11503875

申请日：2006-08-11

申请人： Hsien-cheng Wu ,  Flavio Nardi ,  Jin-Jae Chen ,  Eric Hartman

发明人： Hsien-cheng Wu ,  Flavio Nardi ,  Jin-Jae Chen ,  Eric Hartman

IPC分类号： B60T8/60

CPC分类号： B60W40/11 ,  B60T8/1708 ,  B60T8/1755 ,  B60T2230/06 ,  B60W40/112 ,  B60W40/114 ,  B60W50/06 ,  B60W2300/14 ,  B60W2520/125 ,  B60W2520/14 ,  B60W2520/22 ,  B60W2520/28 ,  B60W2720/14

摘要： A method, and a system using the method, of controlling a towing vehicle that is connected to a vehicle trailer. The method includes sensing a set of vehicle targets and a set of vehicle conditions in response to the set of vehicle targets. The method also includes determining a plurality of differences between the set of vehicle targets and the set of vehicle conditions, determining a trend of the plurality of differences, generating at least one of a symmetric signal and an asymmetric signal based on the trend, and actuating a vehicle system with the at least one of a symmetric signal and an asymmetric signal.

摘要翻译： 一种用于控制连接到车辆拖车的牵引车辆的方法和使用该方法的系统。 所述方法包括响应于所述车辆目标集合来感测一组车辆目标和一组车辆状况。 该方法还包括确定车辆目标集合和车辆状况集合之间的多个差异，确定多个差异的趋势，基于该趋势产生对称信号和不对称信号中的至少一个，并且致动 具有对称信号和不对称信号中的至少一个的车辆系统。

公开(公告)号：US07039475B2

公开(公告)日：2006-05-02

申请号：US10730835

申请日：2003-12-09

申请人： Bijan Sayyarrodsari ,  Eric Hartman ,  Celso Axelrud ,  Kidir Liano

发明人： Bijan Sayyarrodsari ,  Eric Hartman ,  Celso Axelrud ,  Kidir Liano

IPC分类号： G05B13/02

CPC分类号： G05B13/027 ,  G05B13/048 ,  G05B17/02

摘要： The present invention provides a method for controlling nonlinear process control problems. This method involves first utilizing modeling tools to identify variable inputs and controlled variables associated with the process, wherein at least one variable input is a manipulated variable. The modeling tools are further operable to determine relationships between the variable inputs and controlled variables. A control system that provides inputs to and acts on inputs from the modeling tools tunes one or more manipulated variable inputs to achieve a desired result like greater efficiency, higher quality, or greater consistency.

摘要翻译： 本发明提供一种控制非线性过程控制问题的方法。 该方法包括首先利用建模工具来识别与该过程相关联的变量输入和控制变量，其中至少一个可变输入是操纵变量。 建模工具还可操作以确定可变输入和受控变量之间的关系。 为建模工具提供输入和输入的操作的控制系统调整一个或多个操作变量输入，以获得更高效率，更高质量或更高一致性的期望结果。

公开(公告)号：US07020642B2

公开(公告)日：2006-03-28

申请号：US10051574

申请日：2002-01-18

申请人： Bruce Ferguson ,  Eric Hartman

发明人： Bruce Ferguson ,  Eric Hartman

IPC分类号： G06F15/18 ,  G06E1/00

CPC分类号： G06K9/6284 ,  G05B13/048 ,  G05B17/02 ,  G06K9/6269 ,  G06N99/005

摘要： A system and method for preprocessing input data to a support vector machine (SVM). The SVM is a system model having parameters that define the representation of the system being modeled, and operates in two modes: run-time and training. A data preprocessor preprocesses received data in accordance with predetermined preprocessing parameters, and outputs preprocessed data. The data preprocessor includes an input buffer for receiving and storing the input data. The input data may be on different time scales. A time merge device determines a desired time scale and reconciles the input data so that all of the input data are placed on the desired time scale. An output device outputs the reconciled data from the time merge device as preprocessed data. The reconciled data may be input to the SVM in training mode to train the SVM, and/or in run-time mode to generate control parameters and/or predictive output information.

公开(公告)号：US20060249536A1

公开(公告)日：2006-11-09

申请号：US11124145

申请日：2005-05-09

申请人： Eric Hartman ,  Bradley Feldman ,  Wendy Geiger

发明人： Eric Hartman ,  Bradley Feldman ,  Wendy Geiger

IPC分类号： B67D5/56

CPC分类号： B67D1/108 ,  B67D1/0004 ,  B67D1/0021 ,  B67D1/0051 ,  B67D2001/082

摘要： The present invention relates to a food dispensing device for dispensing a food product. The device includes a valve/cannula connection assembly that provides a reclosable resilient sealed connection to a food container. A cannula is received in a resilient valve to open the valve.

摘要翻译： 本发明涉及一种用于分配食品的食物分配装置。 该装置包括阀/套管连接组件，其提供与食物容器的可再闭合的弹性密封连接。 套管被接纳在弹性阀中以打开阀门。

公开(公告)号：US06941301B2

公开(公告)日：2005-09-06

申请号：US10051266

申请日：2002-01-18

申请人： Bruce Ferguson ,  Eric Hartman

发明人： Bruce Ferguson ,  Eric Hartman

IPC分类号： G06E1/00 ,  G06F7/00 ,  G06F15/18 ,  G06F17/30 ,  G06K9/00

CPC分类号： G06K9/00503 ,  Y10S707/99934 ,  Y10S707/99936 ,  Y10S707/99943

摘要： A system and method for preprocessing input data to a support vector machine (SVM). The SVM is a system model having parameters that define the representation of the system being modeled, and operates in two modes: run-time and training. A data preprocessor preprocesses received data in accordance with predetermined preprocessing parameters, and outputs preprocessed data. The data preprocessor includes an input buffer for receiving and storing the input data. The input data may include one or more outlier values. A data filter detects and removes any outlier values in the input data, generating corrected input data. The filter may optionally replace the outlier values in the input data. An output device outputs the corrected data from the data filter as preprocessed data. The corrected data may be input to the SVM in training mode to train the SVM, and/or in run-time mode to generate control parameters and/or predictive output information.

摘要翻译： 一种用于将输入数据预处理到支持向量机（SVM）的系统和方法。 SVM是具有定义正在建模的系统的表示的参数的系统模型，并且以运行时和训练两种模式运行。 数据预处理器根据预定的预处理参数对接收到的数据进行预处理，并输出预处理数据。 数据预处理器包括用于接收和存储输入数据的输入缓冲器。 输入数据可以包括一个或多个异常值。 数据滤波器检测和去除输入数据中的任何异常值，产生校正的输入数据。 滤波器可以可选地替换输入数据中的异常值。 输出装置将来自数据滤波器的校正数据作为预处理数据输出。 校正数据可以在训练模式下输入到SVM，以训练SVM，和/或以运行时模式生成控制参数和/或预测输出信息。

公开(公告)号：US6047221A

公开(公告)日：2000-04-04

申请号：US943489

申请日：1997-10-03

申请人： Stephen Piche ,  James David Keeler ,  Eric Hartman ,  William D. Johnson ,  Mark Gerules ,  Kadir Liano

发明人： Stephen Piche ,  James David Keeler ,  Eric Hartman ,  William D. Johnson ,  Mark Gerules ,  Kadir Liano

IPC分类号： G05B23/02 ,  G05B13/02

CPC分类号： G05B17/02 ,  G05B13/048

摘要： A method for modeling a steady-state network in the absence of steady-state historical data. A steady-state neural network can be tied by impressing the dynamics of the system onto the input data during the training operation by first determining the dynamics in a local region of the input space, this providing a set of dynamic training data. This dynamic training data is then utilized to train a dynamic model, gain thereof then set equal to unity such that the dynamic model is now valid over the entire input space. This is a linear model, and the historical data over the entire input space is then processed through this model prior to input to the neural network during training thereof to remove the dynamic component from the data, leaving the steady-state component for the purpose of training. This provides a valid model in the presence of historical data that has a large content of dynamic behavior. A single dynamic model is required for each output variable in a multi-input multi-output steady-state model such that for each output there is a separate dynamic model required for pre-filtering. They are combined in a single network made up of multiple individual steady-state models for each output. The dynamic model can be identified utilizing a weighting factor for the gain to force the dynamic gain of the dynamic model to the steady-state gain by weighting the difference thereof during optimization of the dynamic model. The steady-state model is optimized utilizing gain constraints during the optimization procedure such that the gain of the network is prevented from exceeding the gain constraints.

摘要翻译： 在没有稳态历史数据的情况下建模稳态网络的方法。 稳态神经网络可以通过在训练操作期间通过首先确定输入空间的局部区域中的动力学来将系统的动力学压印到输入数据上，从而提供一组动态训练数据。 然后利用该动态训练数据来训练动态模型，然后将其增益设置为等于1，使动态模型现在在整个输入空间上有效。 这是一个线性模型，然后在整个输入空间中的历史数据在通过该模型输入到神经网络之前通过该模型进行处理，以在训练期间从数据中移除动态分量，将稳态分量留在目的 训练。 这在存在具有大量动态行为的历史数据的情况下提供了有效的模型。 在多输入多输出稳态模型中，每个输出变量都需要单个动态模型，因此对于每个输出，都需要一个单独的动态模型来进行预滤波。 它们组合在由每个输出的多个单独稳态模型组成的单个网络中。 可以利用增益的加权因子来识别动态模型，以通过在动态模型的优化期间加权其差异来将动态模型的动态增益强制为稳态增益。 在优化过程中利用增益约束优化稳态模型，使得网络的增益被阻止超过增益约束。

公开(公告)号：US07599749B2

公开(公告)日：2009-10-06

申请号：US11678634

申请日：2007-02-26

申请人： Bijan Sayyarrodsari ,  Eric Hartman ,  Celso Axelrud ,  Kadir Liano

发明人： Bijan Sayyarrodsari ,  Eric Hartman ,  Celso Axelrud ,  Kadir Liano

IPC分类号： G05B13/02

CPC分类号： G05B13/042

摘要： The present invention provides a method for controlling nonlinear control problems within particle accelerators. This method involves first utilizing software tools to identify variable inputs and controlled variables associated with the particle accelerator, wherein at least one variable input parameter is a controlled variable. This software tool is further operable to determine relationships between the variable inputs and controlled variables. A control system that provides variable inputs to and acts on controller outputs from the software tools tunes one or more manipulated variables to achieve a desired controlled variable, which in the case of a particle accelerator may be realized as a more efficient collision.

摘要翻译： 本发明提供了一种用于控制粒子加速器内的非线性控制问题的方法。 该方法首先利用软件工具来识别与粒子加速器相关联的变量输入和控制变量，其中至少一个可变输入参数是受控变量。 该软件工具进一步可操作以确定可变输入和受控变量之间的关系。 向软件工具向控制器输出提供可变输入并作用于控制器输出的控制系统调整一个或多个操纵变量以实现期望的受控变量，其在粒子加速器的情况下可被实现为更有效的冲突。

公开(公告)号：US20080061081A1

公开(公告)日：2008-03-13

申请号：US11939449

申请日：2007-11-13

申请人： Arturo Guerrero ,  Balakrishna Reddy ,  Mark Beaumont ,  Eric Hartman

发明人： Arturo Guerrero ,  Balakrishna Reddy ,  Mark Beaumont ,  Eric Hartman

IPC分类号： B67D1/08

CPC分类号： A61L2/22 ,  A61L2/18 ,  A61L2/24 ,  B67D1/07

摘要： A method and device for hygienically delivering a liquid food to or from a food or beverage dispenser by delivering a flow of liquid food through an outlet positioned at a distance of all surfaces of the dispenser and by frequently cleaning the outlet with a flow of cleaning liquid directed onto the external surfaces demarcating the outlet to eliminate the liquid and solid food residue. The device may include a connection fitment with an elongated food delivery member which is configured to connect to a cleaning chamber of the dispenser. The device can be easier and more conveniently cleaned and/or sanitized enabling the dispensing of microbial sensitive food products such as milk concentrate and the like.

摘要翻译： 一种用于通过将液体食物流通过位于分配器的所有表面的距离处的出口传送液体食物到食品或饮料分配器的卫生方式和装置，并且通过以清洗液流频繁地清洁出口 引导到外表面上划分出口以消除液体和固体食物残渣。 该装置可以包括与细长食物输送构件的连接配件，其被配置为连接到分配器的清洁室。 该装置可以更容易且更方便地清洁和/或消毒，使得能够分配微生物敏感食品如奶浓缩物等。