利索-全球专利检索

  1. 登录
  2. 注册

搜索结果

80 条记录 (耗时 0.063 秒)

    SYSTEM AND METHOD FOR EQUIPMENT REMAINING LIFE ESTIMATION
    41.
    发明申请
    SYSTEM AND METHOD FOR EQUIPMENT REMAINING LIFE ESTIMATION 有权
    用于设备的生命周期估计的系统和方法

    公开(公告)号：US20080140361A1

    公开(公告)日：2008-06-12

    申请号：US11608058

    申请日：2006-12-07

    申请人： Piero Patrone Bonissone ,  Feng Xue ,  Anil Varma ,  Kai Frank Goebel ,  Weizhong Yan ,  Neil Holger White Eklund

    发明人： Piero Patrone Bonissone ,  Feng Xue ,  Anil Varma ,  Kai Frank Goebel ,  Weizhong Yan ,  Neil Holger White Eklund

    IPC分类号： G06F17/10

    CPC分类号： G06N5/04

    摘要： A method to predict remaining life of a target is disclosed. The method includes receiving information regarding a behavior of the target, and identifying from a database at least one piece of equipment having similarities to the target. The method further includes retrieving from the database data prior to an end of the equipment useful life, the data having a relationship to the behavior, evaluating a similarity of the relationship, predicting the remaining life of the target based upon the similarity, and generating a signal corresponding to the predicted remaining equipment life.

    摘要翻译： 公开了一种预测目标的剩余寿命的方法。 该方法包括接收关于目标的行为的信息，以及从数据库中识别具有与目标相似的至少一件设备。 该方法还包括在设备使用寿命结束之前从数据库中获取数据，数据与行为有关系，评估关系的相似性，基于相似度预测目标的剩余寿命，并且生成 信号对应于预计的剩余设备寿命。

    Method and system for automated property valuation
    42.
    发明授权
    Method and system for automated property valuation 失效
    自动化物业估值的方法和系统

    公开(公告)号：US06748369B2

    公开(公告)日：2004-06-08

    申请号：US09337285

    申请日：1999-06-21

    申请人： Pratap Shankar Khedkar ,  Piero Patrone Bonissone

    发明人： Pratap Shankar Khedkar ,  Piero Patrone Bonissone

    IPC分类号： G06F1518

    CPC分类号： G06N5/048 ,  G06N3/0436

    摘要： A method and system for automating a process for valuing a property that produces an estimated value of a subject property, and a reliability assessment of the estimated value. The process is a generative artificial intelligence method that trains a fuzzy-neural network using a subset of cases from a case-base, and produces a run-time system to provide an estimate of the subject property's value. A network-based implementation of fuzzy inference is based on a system that implements a fuzzy system as a five-layer neural network so that the structure of the network can be interpreted in terms of high-level rules. The neural network is trained automatically from data. IF/THEN rules are used to map inputs to outputs by a fuzzy logic inference system. Different models for the same problem can be obtained by changing the inputs to the neuro-fuzzy network, or by varying its architecture.

    摘要翻译： 用于自动化产生对象属性的估计值的属性的评估过程的方法和系统以及估计值的可靠性评估。 该过程是一种生成人工智能方法，它使用案例基础案例的子集训练模糊神经网络，并产生一个运行时系统来提供主体属性值的估计。 模糊推理的基于网络的实现是基于将模糊系统实现为五层神经网络的系统，以便可以根据高级规则来解释网络的结构。 从数据自动训练神经网络。 IF / THEN规则用于通过模糊逻辑推理系统将输入映射到输出。 通过改变对神经 - 模糊网络的输入，或通过改变其架构，可以获得同样问题的不同模型。

    System and method for advanced condition monitoring of an asset system
    47.
    发明授权
    System and method for advanced condition monitoring of an asset system 有权
    资产系统高级状态监测系统和方法

    公开(公告)号：US07756678B2

    公开(公告)日：2010-07-13

    申请号：US12129632

    申请日：2008-05-29

    申请人： Piero Patrone Bonissone ,  John Erik Hershey ,  Robert James Mitchell, Jr. ,  Rajesh Venkat Subbu, Jr. ,  Avinash Vinayak Taware ,  Xiao Hu

    发明人： Piero Patrone Bonissone ,  John Erik Hershey ,  Robert James Mitchell, Jr. ,  Rajesh Venkat Subbu, Jr. ,  Avinash Vinayak Taware ,  Xiao Hu

    IPC分类号： G06F11/28

    CPC分类号： G05B23/0254 ,  G05B2219/33295 ,  G05B2219/33296 ,  G07C3/00

    摘要： A method for advanced condition monitoring of an asset system includes using a plurality of auto-associative neural networks to determine estimates of actual values sensed by at least one sensor in at least one of the plurality of operating regimes; determining a residual between the estimated sensed values and the actual values sensed by the at least one sensor from each of the plurality of auto-associative neural networks; and combining the residuals by using a fuzzy supervisory model blender; performing a fault diagnostic on the combined residuals; and determining a change of the operation of the asset system by analysis of the combined residuals. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

    摘要翻译： 一种用于资产系统的高级状态监测的方法包括使用多个自动关联神经网络来确定至少一个传感器在所述多个操作方案中的至少一个中感测到的实际值的估计; 从所述多个自相关神经网络中的每一个确定估计的感测值与由所述至少一个传感器感测的实际值之间的残差; 并通过使用模糊监督模型混合器组合残差; 对组合残差进行故障诊断; 以及通过分析组合残差来确定资产系统的操作变化。 必要时提供警报。 智能传感器系统包括用于执行本发明的方法的车载处理单元。

    SYSTEM AND PROCESS FOR A FUSION CLASSIFICATION FOR INSURANCE UNDERWRITING SUITABLE FOR USE BY AN AUTOMATED SYSTEM
    48.
    发明申请
    SYSTEM AND PROCESS FOR A FUSION CLASSIFICATION FOR INSURANCE UNDERWRITING SUITABLE FOR USE BY AN AUTOMATED SYSTEM 有权
    用于保险分类的系统和程序，适用于自动系统使用的保险

    公开(公告)号：US20090048876A1

    公开(公告)日：2009-02-19

    申请号：US12131545

    申请日：2008-06-02

    申请人： Piero Patrone Bonissone ,  Kareem Sherif AGGOUR ,  Rajesh Venkat SUBBU ,  Weizhong YAN ,  Naresh Sundaram IYER ,  Anindya CHAKRABORTY

    发明人： Piero Patrone Bonissone ,  Kareem Sherif AGGOUR ,  Rajesh Venkat SUBBU ,  Weizhong YAN ,  Naresh Sundaram IYER ,  Anindya CHAKRABORTY

    IPC分类号： G06Q40/00 ,  G06Q10/00

    CPC分类号： G06Q40/08 ,  G06Q40/00

    摘要： A method and system for fusing a collection of classifiers used for an automated insurance underwriting system and/or its quality assurance is described. Specifically, the outputs of a collection of classifiers are fused. The fusion of the data will typically result in some amount of consensus and some amount of conflict among the classifiers. The consensus will be measured and used to estimate a degree of confidence in the fused decisions. Based on the decision and degree of confidence of the fusion and the decision and degree of confidence of the production decision engine, a comparison module may then be used to identify cases for audit, cases for augmenting the training/test sets for re-tuning production decision engine, cases for review, or may simply trigger a record of its occurrence for tracking purposes. The fusion can compensate for the potential correlation among the classifiers. The reliability of each classifier can be represented by a static or dynamic discounting factor, which will reflect the expected accuracy of the classifier. A static discounting factor is used to represent a prior expectation about the classifier's reliability, e.g., it might be based on the average past accuracy of the model, while a dynamic discounting is used to represent a conditional assessment of the classifier's reliability, e.g., whenever a classifier bases its output on an insufficient number of points it is not reliable.

    摘要翻译： 描述用于融合用于自动保险承保系统的分类器集合和/或其质量保证的方法和系统。 具体来说，分类器的集合的输出被融合。 数据的融合通常会导致一些共识和分类器之间的一些冲突。 共识将被测量并用于估计融合决策的信心程度。 根据融合的决定和信心程度以及生产​​决策引擎的决策和决策程度，然后可以使用比较模块来识别审计案例，增加用于重新调整生产的培训/测试集的案例 决策引擎，审查案例，或者可以简单地触发其发生记录以进行跟踪。 融合可以补偿分类器之间的潜在相关性。 每个分类器的可靠性可以由静态或动态折扣因子表示，这将反映分类器的预期准确性。 静态折扣因子用于表示对分类器的可靠性的先前期望，例如，可以基于模型的平均过去精度，而使用动态贴现来表示分类器的可靠性的条件评估，例如，每当 分类器的输出基于不可靠的点数不足。

    Systems and methods for efficient frontier supplementation in multi-objective portfolio analysis
    49.
    发明授权
    Systems and methods for efficient frontier supplementation in multi-objective portfolio analysis 失效
    多目标投资组合分析中有效前沿补充的系统和方法

    公开(公告)号：US07469228B2

    公开(公告)日：2008-12-23

    申请号：US10781897

    申请日：2004-02-20

    申请人： Piero Patrone Bonissone ,  Srinivas Bollapragada ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    发明人： Piero Patrone Bonissone ,  Srinivas Bollapragada ,  Kete Charles Chalermkraivuth ,  Neil Holger White Eklund ,  Naresh Sundaram Iyer ,  Rajesh Venkat Subbu

    IPC分类号： G06Q40/00

    CPC分类号： G06Q40/06

    摘要： The systems and methods of the invention are directed to portfolio optimization and related techniques. For example, the invention provides a method for multi-objective portfolio optimization for use in investment decisions based on competing objectives and a plurality of constraints constituting a portfolio problem, the method comprising: performing a first multi-objective optimization process, based on competing objectives, to generate an efficient frontier of possible solutions; observing the generated efficient frontier; based on the observing, identifying an area of the efficient frontier in which there is a gap; and effecting a gap filling process by which the efficient frontier is supplemented in the area of the gap, the efficient frontier being used in investment decisioning.

    摘要翻译： 本发明的系统和方法针对组合优化和相关技术。 例如，本发明提供了一种用于基于竞争目标和构成投资组合问题的多个约束的投资决策中的多目标投资组合优化的方法，所述方法包括：基于竞争目标执行第一多目标优化过程 ，以产生可能的解决方案的有效前沿; 观察生成的有效边界; 根据观察，确定存在差距的有效边界的一个区域; 并在差距填补过程中，有效的前沿在差距领域得到补充，有效的前沿被用于投资决策。