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    • 1. 发明授权
    • Training a model of a non-linear process
    • 训练非线性过程的模型
    • US08019701B2
    • 2011-09-13
    • US12112750
    • 2008-04-30
    • Bijan Sayyar-RodsariEdward PlumerEric HartmanKadir LianoCelso Axelrud
    • Bijan Sayyar-RodsariEdward PlumerEric HartmanKadir LianoCelso Axelrud
    • G06N5/00
    • G05B13/048G05B13/042G05B17/02
    • 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.
    • 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。
    • 2. 发明申请
    • CONTROLLING A NON-LINEAR PROCESS
    • 控制非线性过程
    • US20080208778A1
    • 2008-08-28
    • US12112847
    • 2008-04-30
    • Bijan Sayyar-RodsariEdward PlumerEric HartmanKadir LianoCelson Axelrud
    • Bijan Sayyar-RodsariEdward PlumerEric HartmanKadir LianoCelson Axelrud
    • G06F15/18G05B13/02
    • G05B13/048G05B13/042G05B17/02
    • 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.
    • 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。
    • 7. 发明授权
    • Pre-processing input data with outlier values for a support vector machine
    • 使用支持向量机的异常值预处理输入数据
    • US06941301B2
    • 2005-09-06
    • US10051266
    • 2002-01-18
    • Bruce FergusonEric Hartman
    • Bruce FergusonEric Hartman
    • G06E1/00G06F7/00G06F15/18G06F17/30G06K9/00
    • G06K9/00503Y10S707/99934Y10S707/99936Y10S707/99943
    • 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.
    • 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。
    • 8. 发明授权
    • Method for steady-state identification based upon identified dynamics
    • 基于确定的动力学的稳态识别方法
    • US6047221A
    • 2000-04-04
    • US943489
    • 1997-10-03
    • Stephen PicheJames David KeelerEric HartmanWilliam D. JohnsonMark GerulesKadir Liano
    • Stephen PicheJames David KeelerEric HartmanWilliam D. JohnsonMark GerulesKadir Liano
    • G05B23/02G05B13/02
    • G05B17/02G05B13/048
    • 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.
    • 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。