公开(公告)号：US20040267681A1

公开(公告)日：2004-12-30

申请号：US10608603

申请日：2003-06-27

Inventor： Paul Savage

IPC: G06E001/00 ,  G06E003/00 ,  G06G007/00 ,  G06F015/18

CPC classification number: G06F17/5045

Abstract: A method of creating a logical device performing polynomial division includes using a hardware description language to build code directly describing synthesizable logic for performing the polynomial division. The logic is then implemented on a target device. The code receives as inputs a parameter identifying a polynomial and a parameter identifying a number of data bits for which the polynomial division is performed. For a given n-degree polynomial, performing the polynomial division includes calculating a next n-term remainder for a data unit having d terms.

公开(公告)号：US20040267680A1

公开(公告)日：2004-12-30

申请号：US10602193

申请日：2003-06-24

Applicant: Palo Alto Research Center Incorporated

Inventor： Markus P.J. Fromherz ,  Lara S. Crawford ,  Yi Shang

IPC: G06E001/00 ,  G06E003/00 ,  G06G007/00 ,  G06F015/18 ,  G06N005/02 ,  G06F017/00

CPC classification number: G05B13/021

Abstract: A method for feedback control of cooperative problem solving for real-time applications in complex systems utilizes solvers parameterized by control variables. The method includes initializing the time setting and selecting at least one solver parameter value. The solver is operated with the selected solver parameter value or values for a specified interim and the operational conditions are reviewed. A solution is transmitted to the system if a solution quality condition is satisfied. The solver continues to operate if the solution quality condition is not satisfied and the performance differential is not greater than a specified threshold. If the solution quality condition is unsatisfied, but the performance differential exceeds the threshold, at least one alternate solver parameter value is selected and the solver is operated with the new solver parameter value for a specified interim. The solver continues to operate until the solution quality condition is satisfied.

公开(公告)号：US20040254902A1

公开(公告)日：2004-12-16

申请号：US10710008

申请日：2004-06-11

Inventor： David Lawrence Von Klleeck

IPC: G06F017/60 ,  G06G007/00 ,  G06F015/18 ,  G06N003/00 ,  G06N003/12 ,  G06E001/00 ,  G06E003/00

CPC classification number: G06Q10/10 ,  G06Q10/1053

Abstract: This invention, relates to a generic system and method for supporting hiring decisions based on biographical information blank input, more particularly, this system and method yields superior decisions through the use of soft computing technologies (fuzzy logic, neural networks, and genetic algorithms) to better score biographical information blanks.

Abstract translation: 本发明涉及一种用于基于传记信息空白输入来支持招聘决定的通用系统和方法，更具体地说，该系统和方法通过使用软计算技术（模糊逻辑，神经网络和遗传算法）向 更好的得分传记信息空白。

公开(公告)号：US20040236709A1

公开(公告)日：2004-11-25

申请号：US10621645

申请日：2003-07-17

Inventor： Blake Johnson ,  Dario Benavides ,  Antje Kann

IPC: G06E001/00 ,  G06E003/00 ,  G06G007/00 ,  G06F015/18 ,  G06N005/00 ,  G06F017/00

CPC classification number: G06Q10/04 ,  G06Q10/06 ,  G06Q30/02

Abstract: A system and method for representing and incorporating available information into uncertainty-based forecasts is provided. The system comprises a new class of models able to efficiently and effectively represent uncertainty-based forecasts with a wide range of characteristics with greater accuracy. Further, methods provide for selection of a most appropriate model from the class of models and calibration of the selected model to all available data, including both directly relevant historical data and expert opinion and analysis. An output is a model that can be used to generate an uncertainty-based forecast for a variable or variables of interest accurately and efficiently. In addition, methods for refining input data and testing and refining the output representation of the uncertainty-based forecast are provided.

Abstract translation: 提供了一种用于将可用信息表示和合并到基于不确定性的预测中的系统和方法。 该系统包括一类新的模型，能够高精度地有效地代表具有广泛特征的基于不确定性的预测。 此外，方法提供从模型类别中选择最合适的模型并将所选模型的校准选择到所有可用数据，包括直接相关的历史数据和专家意见和分析。 输出是一种模型，可以用于准确有效地为变量或感兴趣的变量生成基于不确定性的预测。 此外，还提供了改进输入数据和测试和改进基于不确定性预测的输出表示的方法。

公开(公告)号：US20040225628A1

公开(公告)日：2004-11-11

申请号：US10747039

申请日：2003-12-30

Applicant: INTELLIGENT WAVE, INC.

Inventor： Osamu Aoki ,  Mikinori Seita

IPC: G06F017/00 ,  G06N005/00 ,  G06F015/18 ,  G06G007/00 ,  G06E003/00 ,  G06E001/00 ,  G06F017/60

CPC classification number: G06Q20/4016 ,  G06Q20/10 ,  G06Q20/24 ,  Y10S707/99942

Abstract: A data merging program causes a computer to perform a step of selecting a first cell as a starting point of merging; a step of comparing a first numerical value, which is recorded in the first cell, with a preset reference value; a step of, if the first numerical value is smaller than the reference value, calculating a total value of the first numerical value and a second numerical value recorded in a second cell adjacent to the first cell in the same column; a step of comparing the total value with the reference value and, if the total value is smaller than the reference value, setting a third cell into which the first and second cells are merged and recording the total value in the third cell; and a step of selecting the third cell as a new starting point of merging.

Abstract translation: 数据合并程序使计算机执行选择第一个单元作为合并起点的步骤; 将记录在第一单元中的第一数值与预先设定的基准值进行比较的步骤; 如果第一数值小于参考值，则计算第一数值的总值和记录在与同一列中的第一单元相邻的第二单元中的第二数值的步骤; 将总值与参考值进行比较的步骤，如果总值小于参考值，则设置合并第一和第二单元的第三单元，并记录第三单元中的总值; 以及选择第三个单元格作为新的合并起始点的步骤。

公开(公告)号：US20040220892A1

公开(公告)日：2004-11-04

申请号：US10425463

申请日：2003-04-29

Inventor： Ira Cohen ,  Fabio G. Cozman ,  Alexandre Bronstein ,  Marsha Prescott Duro

IPC: G06E001/00 ,  G06E003/00 ,  G06G007/00 ,  G06F015/18

CPC classification number: G06N7/005 ,  G06N20/00

Abstract: A method that yields more accurate Bayesian network classifiers when learning from unlabeled data in combination with labeled data includes learning a set of parameters for a structure of a classifier using a set of labeled data and learning a set of parameters for the structure using the labeled data and a set of unlabeled data and then modifying the structure if the parameters based on the labeled and unlabeled data leads to less accuracy in the classifier in comparison to the parameters based on the labeled data only. The present technique enable an increase in the accuracy of a statistically learned Bayesian network classifier when unlabeled data are available and reduces the likelihood of degrading the accuracy of the Bayesian network classifier when using unlabeled data.

Abstract translation: 当从未标记的数据与标记数据组合学习时，产生更精确的贝叶斯网络分类器的方法包括使用一组标记数据学习一组分类器的结构参数，并使用标记数据学习一组参数 和一组未标记的数据，然后如果基于标记和未标记的数据的参数相比于仅基于标记数据的参数，分类器中的精度降低，则修改结构。 当未标记的数据可用时，本技术能够增加统计学习的贝叶斯网络分类器的准确性，并且降低在使用未标记的数据时降低贝叶斯网络分类器的精度的可能性。

公开(公告)号：US20040205036A1

公开(公告)日：2004-10-14

申请号：US10476432

申请日：2004-05-27

Inventor： Nagabhushana Prabhu ,  Hung-Chich Chang

IPC: G06E001/00 ,  G06E003/00 ,  G06G007/00 ,  G06F015/18 ,  G06F019/00

CPC classification number: G06K9/6284 ,  G01N23/04

Abstract: The present invention is a system and method of improving computational efficiency of constrained nonlinear problems by utilizing Lie groups and their associated Lie algebras to transform constrained nonlinear problems into equivalent unconstrained problems. A first nonlinear surface including a plurality of points is used to determine a second nonlinear surface that also includes a plurality of points. A reference point is selected from the plurality of points of the second nonlinear surface. An objective function equation is maximized by computing a gradient direction line from the reference point. The reference point is adjusted to the point determined along the gradient direction line having the highest associated value.

Abstract translation: 本发明是通过利用李群及其相关联的李代数将约束非线性问题转化为等效无约束问题来提高约束非线性问题的计算效率的系统和方法。 包括多个点的第一非线性表面用于确定还包括多个点的第二非线性表面。 从第二非线性表面的多个点中选择参考点。 通过从参考点计算梯度方向线来最大化目标函数方程。 参考点被调整为具有最高相关值的梯度方向线所确定的点。

公开(公告)号：US20040199365A1

公开(公告)日：2004-10-07

申请号：US10812868

申请日：2004-03-31

Applicant: Canon Kabushiki Kaisha

Inventor： Teruyoshi Washizawa

IPC: G06F015/18 ,  G05B013/02 ,  G06G007/00 ,  G06E003/00 ,  G06E001/00 ,  G06F017/50

CPC classification number: G06F17/50 ,  G06F2217/08

Abstract: A solution of a structure optimal designing problem formulated as a dual optimization problem having first and second solution processes is obtained. It is assumed that a status variable vector is a displacement in each node and a design variable vector is the existence ratio of structural member in each element. At the first solution process including the second solution process as one step, stored design variable vector and status variable vector are read and the design variable vector is updated. At the second solution process, the stored design variable vector and status variable vector are read and the status variable vector is updated. A second evaluation functional of the second solution process comprises the norm of residual vector and the status variable vector is not initialized upon start of the second solution process. Further, the second solution process is performed by a conjugate gradient method. At the second solution process, preconditioning is performed on a nodal force vector based on a global stiffness matrix, and the design variable vector and status variable vector stored in a second storage are read and the status variable vector is updated. Also, the status variable vector is not initialized upon start of the second solution process.

Abstract translation: 获得了具有第一和第二解决方案的双优化问题的结构最优设计问题的解决方案。 假设状态变量向量是每个节点中的位移，并且设计变量向量是每个元素中结构构件的存在比率。 在第一个解决过程包括第二个解决过程作为一个步骤，读取存储的设计变量向量和状态变量向量，更新设计变量向量。 在第二个解决过程中，读取存储的设计变量向量和状态变量向量，更新状态变量向量。 第二解决过程的第二评估功能包括残差向量的范数，并且状态变量向量在第二解决过程开始时不被初始化。 此外，第二溶液处理通过共轭梯度法进行。 在第二解决过程中，基于全局刚度矩阵对节点力矢量执行预处理，并且读取存储在第二存储器中的设计变量向量和状态变量向量，并更新状态变量向量。 此外，状态变量向量在第二解决方案进程开始时不被初始化。

公开(公告)号：US20040162796A1

公开(公告)日：2004-08-19

申请号：US10748631

申请日：2003-12-30

Inventor： Alex Nugent

IPC: G06G007/00 ,  G06N003/04 ,  G06E003/00 ,  G06E001/00 ,  G06F015/18 ,  G06N003/08

CPC classification number: G06N3/063 ,  B82Y10/00 ,  G06E1/00 ,  G06E3/00 ,  G06G7/00 ,  G06N3/04 ,  G06N3/08 ,  G06N99/007 ,  Y10S977/70 ,  Y10S977/712 ,  Y10S977/742

Abstract: Methods and systems are disclosed herein in which a physical neural network can be configured utilizing nanotechnology. Such a physical neural network can comprise a plurality of molecular conductors (e.g., nanoconductors) which form neural connections between pre-synaptic and post-synaptic components of the physical neural network. Additionally, a learning mechanism can be applied for implementing Hebbian learning via the physical neural network. Such a learning mechanism can utilize a voltage gradient or voltage gradient dependencies to implement Hebbian and/or anti-Hebbian plasticity within the physical neural network. The learning mechanism can also utilize pre-synaptic and post-synaptic frequencies to provide Hebbian and/or anti-Hebbian learning within the physical neural network.

Abstract translation: 本文公开的方法和系统，其中可以使用纳米技术来配置物理神经网络。 这样的物理神经网络可以包括形成物理神经网络的突触前和突触前组件之间的神经连接的多个分子导体（例如，纳米电感器）。 另外，学习机制可以通过物理神经网络实现Hebbian学习。 这种学习机制可以利用电压梯度或电压梯度依赖性在物理神经网络内实现Hebbian和/或抗Hebbian可塑性。 学习机制还可以利用突触前和突触后频率在物理神经网络内提供Hebbian和/或反Hebbian学习。

公开(公告)号：US20040133531A1

公开(公告)日：2004-07-08

申请号：US10393641

申请日：2003-03-21

Inventor： Dingding Chen ,  John A. Quirein ,  Jacky M. Wiener ,  Jeffery L. Grable ,  Syed Hamid ,  Harry D. Smith JR.

IPC: G06N005/02 ,  G06F017/00 ,  G06N005/00 ,  G06E003/00 ,  G06E001/00 ,  G06G007/00 ,  G06F015/18

CPC classification number: G06K9/6298 ,  G01V11/00 ,  G06N3/08

Abstract: A system and method for selecting a training data set from a set of multidimensional geophysical input data samples for training a model to predict target data. The input data may be data sets produced by a pulsed neutron logging tool at multiple depth points in a cases well. Target data may be responses of an open hole logging tool. The input data is divided into clusters. Actual target data from the training well is linked to the clusters. The linked clusters are analyzed for variance, etc. and fuzzy inference is used to select a portion of each cluster to include in a training set. The reduced set is used to train a model, such as an artificial neural network. The trained model may then be used to produce synthetic open hole logs in response to inputs of cased hole log data.

Abstract translation: 一种用于从一组多维地球物理输入数据样本中选择训练数据集的系统和方法，用于训练模型以预测目标数据。 输入数据可以是在情况良好的多个深度点上由脉冲中子测井工具产生的数据集。 目标数据可能是露天测井工具的响应。 输入数据被分成簇。 来自训练井的实际目标数据与集群有关。 对链接的聚类进行方差分析等，并使用模糊推理来选择每个聚类的一部分以包括在训练集中。 缩减集用于训练模型，如人造神经网络。 然后可以使用经过训练的模型来响应于套管孔日志数据的输入而产生合成开孔日志。