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43 条记录 (耗时 0.024 秒)

    Systems and methods for multi-objective portfolio analysis and decision-making using visualization techniques
    32.
    发明授权
    Systems and methods for multi-objective portfolio analysis and decision-making using visualization techniques 有权
    使用可视化技术进行多目标投资组合分析和决策的系统和方法

    公开(公告)号：US07630928B2

    公开(公告)日：2009-12-08

    申请号：US10781871

    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 sequentially comprising: generating a non-dominated solution set in a space; applying a first set of user-specified constraints to reduce the solutions in the non-dominated solution set to a solution subset; and executing a series of local tradeoffs on the solution subset to result in a resulting solution subset, the local tradeoffs being performed in a lower dimension performance space as compared to the space, and the solution subset 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，所述方法顺序地包括：在空间中生成非主导解集; 应用第一组用户指定的约束以将非主导解集合中的解减少到解子集; 并且在解决方案子集上执行一系列局部权衡以产生最终的解决方案子集，与空间相比较，在较低维度的性能空间中执行本地权衡，以及在投资决策中使用的解决方案子集。

    Systems and methods for multi-level optimizing control systems for boilers
    35.
    发明授权
    Systems and methods for multi-level optimizing control systems for boilers 有权
    锅炉多级优化控制系统的系统和方法

    公开(公告)号：US07389151B2

    公开(公告)日：2008-06-17

    申请号：US11276559

    申请日：2006-03-06

    申请人： Vivek Venugopal Badami ,  Rajesh Venkat Subbu ,  Avinash Vinayak Taware ,  Piero Patrone Bonissone ,  Neil Colin Widmer

    发明人： Vivek Venugopal Badami ,  Rajesh Venkat Subbu ,  Avinash Vinayak Taware ,  Piero Patrone Bonissone ,  Neil Colin Widmer

    IPC分类号： G05B13/02 ,  G05D3/12

    CPC分类号： F01K13/02

    摘要： Systems and methods for multi-level optimization of emission levels and efficiency for a boiler system that includes creating both boiler-level models and burner-level models and receiving a plurality of boiler-level system variables. The received system variables are used along with boiler system constraints to optimize boiler-level setpoints. Once the boiler-level setpoints have been optimized they are sent to the burner level of a hierarchical control system, where they are used to optimize burner-level setpoints. Once the burner-level setpoints have been optimized they are sent to the burner control loops of the plant control system to be implemented.

    摘要翻译： 锅炉系统的排放水平和效率的多级优化的系统和方法，包括建立锅炉级模型和燃烧器级模型以及接收多个锅炉级系统变量。 所接收的系统变量与锅炉系统限制一起使用，以优化锅炉级设定值。 一旦锅炉级设定值被优化，就将其发送到分层控制系统的燃烧器级别，用于优化燃烧器级设定值。 一旦燃烧器级设定值已被优化，就将其发送到要实施的设备控制系统的燃烧器控制回路。

    Systems and methods for multi-objective portfolio analysis using pareto sorting evolutionary algorithms
    36.
    发明授权
    Systems and methods for multi-objective portfolio analysis using pareto sorting evolutionary algorithms 有权
    使用帕累托分类进化算法进行多目标投资组合分析的系统和方法

    公开(公告)号：US08219477B2

    公开(公告)日：2012-07-10

    申请号：US10781805

    申请日：2004-02-20

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

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

    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: generating an initial population of solutions of portfolio allocations; committing the initial population of solutions to an initial population archive; performing a multi-objective process, based on the initial population archive and on multiple competing objectives, to generate an efficient frontier, the multi-objective process including a evolutionary algorithm process, the evolutionary algorithm process utilizing a dominance filter, 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：生成投资组合分配的最初解决方案群体; 将最初的人口解决方案提交给初始人口档案; 基于初始人口档案和多个竞争目标，进行多目标过程，以产生有效的前沿，多目标过程包括进化算法过程，利用优势过滤器的进化算法过程，使用的有效边界 投资决策。

    Method and system for fault accommodation of machines
    38.
    发明授权
    Method and system for fault accommodation of machines 有权
    机器故障调节方法及系统

    公开(公告)号：US07904282B2

    公开(公告)日：2011-03-08

    申请号：US11689874

    申请日：2007-03-22

    申请人： Kai Frank Goebel ,  Rajesh Venkat Subbu ,  Randal Thomas Rausch ,  Dean Kimball Frederick

    发明人： Kai Frank Goebel ,  Rajesh Venkat Subbu ,  Randal Thomas Rausch ,  Dean Kimball Frederick

    IPC分类号： G06F9/455

    CPC分类号： F02C9/00 ,  F05D2270/44 ,  F05D2270/71 ,  Y02T50/671

    摘要： A method for multi-objective fault accommodation using predictive modeling is disclosed. The method includes using a simulated machine that simulates a faulted actual machine, and using a simulated controller that simulates an actual controller. A multi-objective optimization process is performed, based on specified control settings for the simulated controller and specified operational scenarios for the simulated machine controlled by the simulated controller, to generate a Pareto frontier-based solution space relating performance of the simulated machine to settings of the simulated controller, including adjustment to the operational scenarios to represent a fault condition of the simulated machine. Control settings of the actual controller are adjusted, represented by the simulated controller, for controlling the actual machine, represented by the simulated machine, in response to a fault condition of the actual machine, based on the Pareto frontier-based solution space, to maximize desirable operational conditions and minimize undesirable operational conditions while operating the actual machine in a region of the solution space defined by the Pareto frontier.

    摘要翻译： 公开了一种使用预测建模的多目标故障调节方法。 该方法包括使用模拟故障实际机器的仿真机，并使用模拟实际控制器的仿真控制器。 基于模拟控制器的指定控制设置和由模拟控制器控制的模拟机器的指定操作场景，执行多目标优化过程，生成与仿真机器的性能相关的基于Pareto前沿的解空间， 模拟控制器，包括对操作场景的调整，以表示模拟机器的故障状态。 实际控制器的控制设置由模拟控制器进行调整，用于根据基于Pareto前沿解决方案空间的实际机器的故障情况来控制由仿真机器表示的实际机器，以最大化 期望的操作条件，并且在由帕累托边界限定的解空间的区域中操作实际机器时最小化不期望的操作条件。

    System and method for implementing a multi objective evolutionary algorithm on a programmable logic hardware device
    40.
    发明授权
    System and method for implementing a multi objective evolutionary algorithm on a programmable logic hardware device 有权
    用于在可编程逻辑硬件设备上实现多目标演化算法的系统和方法

    公开(公告)号：US07809657B2

    公开(公告)日：2010-10-05

    申请号：US11485101

    申请日：2006-07-12

    申请人： Rajesh Venkat Subbu ,  Stefano Romoli Bonissone

    发明人： Rajesh Venkat Subbu ,  Stefano Romoli Bonissone

    IPC分类号： G06N5/00

    CPC分类号： G06N3/126

    摘要： A system for implementing a multi objective evolutionary algorithm (MOEA) on a programmable hardware device is provided. The system comprises a random number generator, a population generator, a crossover/mutation module, a fitness evaluator, a dominance filter and an archive. The random number generator is configured to generate a sequence of pseudo random numbers. The population generator is configured to generate a population of solutions based on the output from the random number generator. The crossover/mutation module is configured to adapt the population of solutions to generate an adapted population of solutions. The fitness evaluator is configured to evaluate each member comprising the population of solutions and the adapted population of solutions. The fitness evaluator is implemented on the programmable hardware device. The dominance filter is configured to select a subset of members from the population of solutions and the adapted population of solutions and generate a filtered population of solutions. The archive configured to store populations of solutions.

    摘要翻译： 提供了一种在可编程硬件设备上实现多目标演化算法（MOEA）的系统。 该系统包括随机数生成器，总体生成器，交叉/变异模块，健身评估器，优势过滤器和存档。 随机数生成器被配置为生成伪随机数序列。 种群生成器被配置为基于来自随机数生成器的输出来生成解的群体。 交叉/变异模块被配置为适应解决方案的群体以产生适应的解决方案群体。 健身评估器被配置为评估包括解决方案群体和适应的解决方案群体的每个成员。 健身评估器在可编程硬件设备上实现。 优势过滤器被配置为从解决方案群体和适应的解决方案群体中选择成员的子集，并生成经过滤波的解决方案群体。 归档配置为存储解决方案的数量。