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1. 发明申请

WO2002091117A2 PROCESS CONTROL LOOP ANALYSIS SYSTEM 审中-公开
标题翻译：过程控制环路分析系统
公开(公告)号：WO2002091117A2
公开(公告)日：2002-11-14
申请号：PCT/US2002/014074
申请日：2002-05-06
申请人： INVENSYS SYSTEMS, INC. , GRUMELART, Alain
发明人： GRUMELART, Alain
IPC分类号： G06F
CPC分类号： G05B23/0216 , G05B21/02 , Y10S706/906 , Y10S707/99937 , Y10S707/99943
摘要： A system and method which allow a user to retrieve data (125) from various process control loops and organize that data in a dynamic manner to allow for multiple types of data analysis. A user may associate individual process control loops into groups and analyze the data and impact of select process control loops in those groups (145). Since the associations of process control loops into groups can be done dynamically (125, 145), the user is able to easily reconfigure groups (i.e. add or remove process control loops) and redo the analysis. Another feature is the storage of both the data retrieved and the results of the analysis so that comparisons can be performed and reports can be generated.
摘要翻译：一种允许用户从各种过程控制循环检索数据（125）并以动态方式组织该数据以允许多种类型的数据分析的系统和方法。用户可以将各个过程控制回路关联到组中，并分析这些组中选择过程控制回路的数据和影响（145）。由于过程控制循环到组中的关联可以动态完成（125,145），用户能够轻松地重新配置组（即添加或删除过程控制循环）并重新进行分析。另一个特征是存储检索的数据和分析结果，以便可以执行比较并生成报告。

2. 发明申请

WO1997037435A2 TRAINABLE, STATE-SAMPLED, NETWORK CONTROLLER 审中-公开
标题翻译：可维护，状态采样，网络控制器
公开(公告)号：WO1997037435A2
公开(公告)日：1997-10-09
申请号：PCT/US1997005162
申请日：1997-03-28
申请人： SMITH, Jay, L.
IPC分类号： H04B00/00
CPC分类号： G06N3/10 , G05B13/0265 , G06N3/063 , Y10S706/903 , Y10S706/906 , Y10S706/907
摘要： A trainable, state-sampled, network controller (TSSNC) or state-sampled controller (SSC) requires little information regarding a plant (as with neural networks), but can use what information is available (as in classical controllers), and provides a linear network (as for CMAC) improving calculation speeds. A form of a governing differential equation characterizing a plant may include parameters and their derivatives of various orders as variables combined in linear and nonlinear terms. Classical control theory, and a method such as a Fourier transform of governing equations, may provide a form of a control law, linear in certain weights or coefficients. Knowledge of coefficients is not required for either the form of the governing equations or the form of the control law. An optimization method may be used to train the SSC, defining a table of weights (contributions to coefficients) to be used in the matrix equation representing the control law the solution yielding a control output to the plant. Sampling plant outputs, during training, may be done at a selected spatial frequency in state space (each dimension a variable from the control law). Sampling is used to provide ideal interpolation of the weights over the entire range of interest. Minimum memory is used with maximum accuracy of interpolation, and any control/output value may be calculated as needed in real-time by a minimal processor.
摘要翻译：可训练的，状态采样的网络控制器（TSSNC）或状态采样控制器（SSC）需要很少关于工厂的信息（如与神经网络一样），但可以使用可用的信息（如在经典控制器中），并且提供线性网络（如CMAC）提高计算速度。表征植物的控制微分方程的形式可以包括作为以线性和非线性项组合的变量的各种阶数的参数及其导数。经典控制理论和诸如控制方程的傅立叶变换的方法可以提供一种控制律的形式，在某些权重或系数中是线性的。对于控制方程的形式或控制规则的形式，不需要知道系数。可以使用优化方法来训练SSC，在表示控制律的矩阵方程中定义要用于表示对工厂的控制输出的解的权重（对系数的贡献）表。在训练期间，采样工厂输出可以在状态空间中选定的空间频率（每个维度与控制规律的变量）完成。采样用于提供整个感兴趣范围内的权重的理想插值。最小内存使用最大的插值精度，任何控制/输出值可以根据需要通过最小的处理器实时计算。

3. 发明申请

WO1993013485A1 CLAUSE COUNTER MAP INFERENCE ENGINE 审中-公开
标题翻译：条款计数器地图信息引擎
公开(公告)号：WO1993013485A1
公开(公告)日：1993-07-08
申请号：PCT/US1992011239
申请日：1992-12-28
申请人： THE UNIVERSITY OF AKRON
发明人： THE UNIVERSITY OF AKRON , WELCH, John, T.
IPC分类号： G06F15/00
CPC分类号： G05B19/052 , G06N5/046 , Y10S706/903 , Y10S706/904 , Y10S706/906
摘要： The invention describes a clause counter map inference engine (50) which is an interpreter method based upon the Boolean form, in which Boolean inference is directly assisted by a programmable data structure. The system provides a translation of a series of Boolean logic functions into a compact map representation (52) which may then be used for Boolean inference by means of an interpreter algorithm (54) or inference engine adapted to update function values based upon any changed input value. The invention provides a rule-based model for logical control systems having an imbeddable, event-chaining inference engine which is independent of the rule-based model. The time to complete a scan cycle of the system depends directly upon the number of inferences in an up-date interval between input and output variable changes, and the lengths of inference chains of events in the logic.

4. 发明申请

WO1992002866A1 COMPUTER NEURAL NETWORK PROCESS MEASUREMENT AND CONTROL SYSTEM AND METHOD 审中-公开
标题翻译：计算机神经网络过程测量与控制系统与方法
公开(公告)号：WO1992002866A1
公开(公告)日：1992-02-20
申请号：PCT/US1991005259
申请日：1991-07-25
申请人： E.I. DU PONT DE NEMOURS & CO. (INC.)
发明人： E.I. DU PONT DE NEMOURS & CO. (INC.) , SKEIRIK, Richard, D.
IPC分类号： G05B13/02
CPC分类号： G05B13/027 , Y10S706/906
摘要： A computer neural network process control system and method provides predicted values to a controller used to control a process for producing a product. The predicted value is produced by a neural network provided with stored input data from a historical database. The neural network is configured by a developer using a neural network configuration stage and step. The neural network is trained using training data stored in the historical database, which is compared with the predicted value produced by the neural network using input data obtained at a time associated with the time of the training data. An error signal is produced by the comparison of the predicted value with the training data, the error signal is used to train the neural network, and is also used to determine (1) whether the predicted values of the neural network should continue to be used by the controller, and (2) to retrain the network. Predicted values are thus produced by the neural network which have a close correlation to the training values training data.

5. 发明申请

WO1990010898A1 SUPPORTING METHOD AND SYSTEM FOR PROCESS CONTROL 审中-公开
标题翻译：支持方法和系统进程控制
公开(公告)号：WO1990010898A1
公开(公告)日：1990-09-20
申请号：PCT/JP1990000322
申请日：1990-03-13
申请人： HITACHI, LTD. , BABA, Kenji , ENBUTSU, Ichiro , WATANABE, Shoji , YAHAGI, Hayao , MARUHASHI, Fumio , MATSUZAKI, Harumi , MATSUMOTO, Hiroshi , NOGITA, Shunsuke , YODA, Mikio , HARA, Naoki
发明人： HITACHI, LTD.
IPC分类号： G05B13/02
CPC分类号： G05B13/027 , C02F1/008 , G05B13/0285 , Y10S706/903 , Y10S706/906
摘要： This invention discloses a method and a system of making a neuron network model learn the typical past process control results and utilizing this model for the supporting of an operation of a process. The neuron network is given a typical pattern of a plurality of input variables at different points in time as input signals, and is taught corresponding control variables as teaching signals, and untaught input patterns are given to this intelligent neuron network model to have it determine corresponding control variables. A plurality of patterns at predetermined time intervals are preferably used at once as patterns to be taught.

6. 发明申请

WO2004109468A3 METHOD FOR IMPLEMENTING INDIRECT CONTROLLER 审中-公开
标题翻译：实施间接控制器的方法
公开(公告)号：WO2004109468A3
公开(公告)日：2005-06-30
申请号：PCT/US2004017688
申请日：2004-06-04
申请人： NEUCO INC , LEFEBVRE CURT W , KOHN DANIEL W
发明人： LEFEBVRE CURT W , KOHN DANIEL W
IPC分类号： G05B7/02 , G05B17/02 , G05B13/02 , G05B11/01 , G05B13/00 , G06E1/00 , G06E3/00 , G06F7/00 , G06F9/44 , G06F15/18 , G06F19/00 , G06G7/00 , G06N3/02 , G06N7/02 , G06N7/06
CPC分类号： G05B7/02 , G05B17/02 , Y10S706/903 , Y10S706/906 , Y10S706/914
摘要： A system and method for implementing an indirect controller (120) for a plant (110). A plant (110) can be provided with both a direct controller and an indirect controller (120) with a system model or a committee of system models. When the system model has sufficient integrity to satisfy the plant requirements, i.e., when the system model has been sufficiently trained, the indirect controller with the system model is automatically enabled to replace the direct controller. When the performance falls, the direct controller can automatically assume operation of the plant (110), preferably maintaining operation in a control region suitable for generating additional training data for the system model. Alternatively, the system model incorporates a committee of models. Various types of sources for errors in the committee of models can be detected and used to implement strategies to improve the quality of the committee.
摘要翻译：一种用于实现植物（110）的间接控制器（120）的系统和方法。工厂（110）可以具有直接控制器和间接控制器（120）与系统模型或系统模型委员会。当系统模型具有足够的完整性以满足工厂要求时，即当系统模型已被充分训练时，具有系统模型的间接控制器被自动地使能以代替直接控制器。当性能下降时，直接控制器可以自动地承担设备（110）的操作，优选地在适于为系统模型产生附加训练数据的控制区域中保持操作。或者，系统模型包含模型委员会。可以检测出各种类型的模型委员会错误来源，并用于实施提高委员会质量的战略。

7. 发明申请

WO1992007311A1 UNIVERSAL PROCESS CONTROL USING ARTIFICIAL NEURAL NETWORKS 审中-公开
标题翻译：通过人工神经网络的通用过程控制
公开(公告)号：WO1992007311A1
公开(公告)日：1992-04-30
申请号：PCT/US1991007518
申请日：1991-10-11
申请人： WESTERN THUNDER
发明人： WESTERN THUNDER , LU, Yong-Zai , CHENG, George, Shu-Xing , MANOFF, Michael
IPC分类号： G05B13/02
CPC分类号： G05B13/027 , Y10S706/906
摘要： Adaptive control for a wide variety of complex processes is provided by an ANN controller (Figures 1a, 1b and 3) and hidden layers having a plurality of neurons (Figure 3) and an output layer with a single neuron (Figure 3). The inputs to the ANN (Figure 3) are a time sequence of error values, and the neuron paths (Figure 4) are weighted as a function of these error values and the present-time process output. The present-time error value may be added to the output layer of the ANN (Figure 3) provide faster response to sudden input changes. The controller of this invention can efficiently handle processes with nonlinear, time-varying, coupled and variable-structure behaviours as well as process parameter and/or structure uncertainties. Large steady-state gains in the process can be compensated by attenuating the ANN block output (Figure 3).
摘要翻译： ANN控制器（图1a，1b和3）和具有多个神经元（图3）的隐层和具有单个神经元的输出层（图3）提供了各种复杂过程的自适应控制。 ANN的输入（图3）是误差值的时间序列，神经元路径（图4）被加权为这些误差值和当前时间过程输出的函数。当前时间误差值可以被添加到ANN的输出层（图3），从而对突然的输入变化提供更快的响应。本发明的控制器可以有效地处理具有非线性，时变，耦合和可变结构行为以及过程参数和/或结构不确定性的过程。通过衰减ANN块输出可以补偿该过程中的大稳态增益（图3）。

8. 发明申请

WO9202895A3 COMPUTER NEURAL NETWORK REGULATORY PROCESS CONTROL SYSTEM AND METHOD 审中-公开
标题翻译：计算机神经网络调节过程控制系统及方法
公开(公告)号：WO9202895A3
公开(公告)日：1992-03-19
申请号：PCT/US9105256
申请日：1991-07-25
申请人： DU PONT
发明人： SKEIRIK RICHARD D
IPC分类号： G06N3/04 , G06F15/80
CPC分类号： G06N3/0427 , Y10S706/906
摘要： A computer neural network regulatory process control system and method allows for the elimination of a human operator from real time control of the process. The present invention operates in three modes: training, operation (prediction), and retraining. In the training mode, training input data is produced by the control adjustment made to the process by the human operator. The neural network of the present invention is trained by producing output data using input data for prediction. The output data is compared with the training input data to produce error data, which is used to adjust the weight(s) of the neural network. When the error data is less than a preselected criterion, training has been completed. In the operation mode, the neural network of the present invention provides output data based upon predictions using the input data. The output data is used to control a state of the process via an actuator. In the retraining mode, retraining data is supplied by monitoring the supplemental actions of the human operator. The retraining data is used by the neural network for adjusting the weight(s) of the neural network.
摘要翻译：计算机神经网络调节过程控制系统和方法允许消除操作人员对过程的实时控制。本发明以三种模式操作：训练，操作（预测）和再训练。在训练模式中，训练输入数据是由操作员对过程进行的控制调整产生的。通过使用用于预测的输入数据产生输出数据来训练本发明的神经网络。将输出数据与训练输入数据进行比较以产生误差数据，其用于调整神经网络的权重。当错误数据低于预选标准时，培训已经完成。在操作模式中，本发明的神经网络基于使用输入数据的预测来提供输出数据。输出数据用于通过执行器控制过程的状态。在再训练模式中，再训练数据通过监控操作员的补充动作来提供。再训练数据由神经网络用于调整神经网络的权重。

9. 发明申请

WO9202863A3 NEURAL NETWORK/EXPERT SYSTEM PROCESS CONTROL SYSTEM AND METHOD 审中-公开
标题翻译：神经网络/专家系统过程控制系统及方法
公开(公告)号：WO9202863A3
公开(公告)日：1992-03-19
申请号：PCT/US9105257
申请日：1991-07-25
申请人： DU PONT
发明人： SKEIRIK RICHARD D
IPC分类号： G05B13/02 , G06N3/04 , G05B
CPC分类号： G06N3/0427 , G05B13/029 , Y10S706/906
摘要： A neural network/expert system process control system and method combines the decision-making capabilities of expert systems with the predictive capabilities of neural networks for improved process control. Neural networks provide predictions of measurements which are difficult to make, or supervisory or regulatory control changes which are difficult to implement using classical control techniques. Expert systems make decisions automatically based on knowledge which is well-known and can be expressed in rules or other knowledge representation forms. Sensor and laboratory data is effectively used. In one approach, the output data from the neural network can be used by the controller in controlling the process, and the expert system can make a decision using sensor or lab data to control the controller(s). In another approach, the output data of the neural network can be used by the expert system in making its decision, and control of the process carried out using lab or sensor data. In another approach, the output data can be used both to control the process and to make decisions.
摘要翻译：神经网络/专家系统过程控制系统和方法将专家系统的决策能力与神经网络的预测能力相结合，以改善过程控制。神经网络提供对难以实现的测量的预测，或使用经典控制技术难以实现的监督或监管控制变化。专家系统根据众所周知的知识自动做出决定，并且可以用规则或其他知识表示形式来表达。传感器和实验室数据得到有效使用。在一种方法中，控制器可以使用来自神经网络的输出数据来控制过程，并且专家系统可以使用传感器或实验室数据来做出决定以控制控制器。在另一种方法中，专家系统可以使用神经网络的输出数据进行决策，并使用实验室或传感器数据对过程进行控制。在另一种方法中，输出数据可以用来控制过程并做出决定。

10. 发明申请

WO1988001775A1 MACROMOLECULE CONSTRUCTION METHODS 审中-公开
标题翻译：大分子结构方法
公开(公告)号：WO1988001775A1
公开(公告)日：1988-03-10
申请号：PCT/US1987002211
申请日：1987-09-02
申请人： GENEX CORPORATION
发明人： GENEX CORPORATION , LADNER, Robert, Charles , HARDMAN, Karl, David
IPC分类号： G06F15/46
CPC分类号： C07K1/00 , G06F19/16 , G06F19/22 , Y10S706/906
摘要： A computer-assisted method for constructing a polypeptide chain having a substantially predetermined conformation (2110). Preferably a known stable well-mapped polypeptide structure is used as a starting point (2120), and additional peptide units are added on (2140) while maintaining favorable enthalpic and entropic contributions to stability (2160). Preferably a library of oligopeptide blocks is used to provide candidates for the additional peptide units. Preferably the library includes numerous precomputed parameters for each of the blocks, e.g. parameters for estimating energetic effects of varying the conformation parameters.

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