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1.发明授权公开(公告)号：US08428985B1
公开(公告)日：2013-04-23
申请号：US12554643
申请日：2009-09-04
申请人： Gintaras Vincent Puskorius , Bryan Roger Goodman , Robbie J. Aniol , William Richard Fravel , Yu-Ning Liu , Melinda Kaye Hunsaker , Shigeru Sadakane
发明人： Gintaras Vincent Puskorius , Bryan Roger Goodman , Robbie J. Aniol , William Richard Fravel , Yu-Ning Liu , Melinda Kaye Hunsaker , Shigeru Sadakane
IPC分类号： G06Q10/00
CPC分类号： G06Q10/06315 , G06Q10/00
摘要： A data store includes, for a dealer of interest, a target inventory mix rate for an item having a feature. A computing device is configured to perform an optimization to obtain a recommended feature allocation of the item including the feature by minimizing a difference between a projected inventory mix rate for the feature and the target inventory mix rate for the feature.
摘要翻译： 对于感兴趣的经销商，数据存储包括具有特征的物品的目标库存混合率。 计算设备被配置为执行优化以通过最小化特征的预计库存混合速率与特征的目标库存混合速率之间的差异来获得包括特征的料品的推荐特征分配。
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公开(公告)号：US08812375B2
公开(公告)日：2014-08-19
申请号：US13268276
申请日：2011-10-07
IPC分类号： G06Q30/00
CPC分类号： G06Q30/0621
摘要： A computer-implemented method or system that is capable of configuring a product. The computer-implemented method or system receiving a product configuration matrix from a computer storage medium. The computer-implemented method or system also receiving user input defining a user selection of at least one feature or component of the product. The computer-implemented method or system generating a binary representation of the user selection. The computer-implemented method or system deriving a resolution matrix. The method or system comparing the resolution matrix and the binary representation of the user selection to determine whether one or more of the selected product features or components in the user selection are valid for the product. The computer-implemented method or system also generating a validation result that identifies one or more valid product features or components within the user selection.
摘要翻译： 能够配置产品的计算机实现的方法或系统。 计算机实现的方法或系统从计算机存储介质接收产品配置矩阵。 计算机实现的方法或系统还接收定义用户选择产品的至少一个特征或组件的用户输入。 计算机实现的方法或系统生成用户选择的二进制表示。 计算机实现的方法或系统导出分辨率矩阵。 比较分辨率矩阵和用户选择的二进制表示的方法或系统以确定用户选择中的所选产品特征或组件中的一个或多个是否对产品有效。 计算机实现的方法或系统还生成识别用户选择内的一个或多个有效产品特征或组件的验证结果。
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公开(公告)号：US20130091033A1
公开(公告)日：2013-04-11
申请号：US13268276
申请日：2011-10-07
IPC分类号： G06Q30/00
CPC分类号： G06Q30/0621
摘要： A computer-implemented method or system that is capable of configuring a product. The computer-implemented method or system receiving a product configuration matrix from a computer storage medium. The computer-implemented method or system also receiving user input defining a user selection of at least one feature or component of the product. The computer-implemented method or system generating a binary representation of the user selection. The computer-implemented method or system deriving a resolution matrix. The method or system comparing the resolution matrix and the binary representation of the user selection to determine whether one or more of the selected product features or components in the user selection are valid for the product. The computer-implemented method or system also generating a validation result that identifies one or more valid product features or components within the user selection.
摘要翻译： 能够配置产品的计算机实现的方法或系统。 计算机实现的方法或系统从计算机存储介质接收产品配置矩阵。 计算机实现的方法或系统还接收定义用户选择产品的至少一个特征或组件的用户输入。 计算机实现的方法或系统生成用户选择的二进制表示。 计算机实现的方法或系统导出分辨率矩阵。 比较分辨率矩阵和用户选择的二进制表示的方法或系统以确定用户选择中的所选产品特征或组件中的一个或多个是否对产品有效。 计算机实现的方法或系统还生成识别用户选择内的一个或多个有效产品特征或组件的验证结果。
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公开(公告)号：US5781700A
公开(公告)日：1998-07-14
申请号：US597080
申请日：1996-02-05
CPC分类号： G05B13/027 , F02D41/1405 , F02D41/1473 , F02D41/2432 , F02D41/2454 , F02D2041/1423 , F02D2041/1429 , F02D2041/1433 , F02D41/2477 , Y10S706/905
摘要： An electronic engine control (EEC) module executes both open loop and closed loop neural network processes to control the air/fuel mixture ratio of a vehicle engine to hold the fuel mixture at stoichiometry. The open loop neural network provides transient air/fuel control to provide a base stoichiometric air/fuel mixture ratio signal in response to throttle position under current engine speed and load conditions. The base air/fuel mixture ratio signal from the open loop network is additively combined with a closed loop trimming signal which varies the air/fuel mixture ratio in response to variations in the sensed exhaust gas oxygen level. Each neural network function is defined by a unitary data structure which defines the network architecture, including the number of node layers, the number of nodes per layer, and the interconnections between nodes. In addition, the data structure holds weight values which determine the manner in which network signals are combined. The network definition data structures are created by a network training system which utilizes an external training processor which employs gradient methods to derive network weight values in accordance with a cost function which quantitatively defines system objectives and an identification network which is pretrained to provide gradient signals representative of the behavior of the physical plant. The training processor executes training cycles asynchronously with the operation of the EEC module in a representative test vehicle.
摘要翻译： 电子发动机控制（EEC）模块执行开环和闭环神经网络过程，以控制车辆发动机的空气/燃料混合比，以将燃料混合物保持在化学计量比。 开环神经网络提供瞬时空气/燃料控制，以在当前发动机转速和负载条件下响应于节气门位置提供基本化学计量的空气/燃料混合比信号。 来自开环网络的基本空气/燃料混合比信号与闭环修整信号相加地组合，所述闭环修整信号响应于所检测的废气氧气水平的变化而改变空气/燃料混合比。 每个神经网络功能由定义网络架构的单一数据结构定义，包括节点层的数量，每层节点的数量以及节点之间的互连。 此外，数据结构保存了确定网络信号组合方式的权重值。 网络定义数据结构由网络训练系统创建，该网络训练系统利用外部训练处理器，该外部训练处理器采用梯度方法根据量化定义系统目标的成本函数和预训练的识别网络来提供梯度信号， 的物理植物的行为。 训练处理器在代表性测试车辆中与EEC模块的操作异步执行训练周期。
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公开(公告)号：US5745653A
公开(公告)日：1998-04-28
申请号：US596535
申请日：1996-02-05
CPC分类号： F02D41/1405 , F02D41/2406 , F02D2041/1423 , F02D2041/1429 , F02D2041/1433
摘要： A electronic engine control (EEC) module executes a generic neural network processing program to perform one or more neural network control funtions. Each neural network funtion is defined by a unitary data structure which defines the network architecture, including the number of node layers, the number of nodes per layer, and the interconnections between nodes. In addition, the data structure holds weight values which determine the manner in which network signals are combined. The network definition data structures are created by a network training system which utilizes an external training processor which employs gradient methods to derive network weight values in accordance with a cost function which quantitatively defines system objectives and an identification network which is pretrained to provide gradient signals representative the behavior of the physical plant. The training processor executes training cycles asynchronously with the operation of the EEC module in a representative test vehicle.
摘要翻译： 电子引擎控制（EEC）模块执行通用神经网络处理程序来执行一个或多个神经网络控制功能。 每个神经网络功能由定义网络架构的单一数据结构定义，包括节点层的数量，每层节点的数量以及节点之间的互连。 此外，数据结构保存了确定网络信号组合方式的权重值。 网络定义数据结构由网络训练系统创建，网络训练系统利用外部训练处理器，该外部训练处理器采用梯度方法根据量化定义系统目标的成本函数和预培训的识别网络来提供梯度信号， 物理植物的行为。 训练处理器在代表性测试车辆中与EEC模块的操作异步执行训练周期。
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公开(公告)号：US6092018A
公开(公告)日：2000-07-18
申请号：US597095
申请日：1996-02-05
CPC分类号： F02D31/005 , F02D37/02 , F02D41/1405
摘要： A electronic engine control (EEC) module executes a neural network processing program to control the idle speed of an internal combustion engine by controlling the bypass air (throttle duty cycle) and the engine's ignition timing. The neural network is defined by a unitary data structure which defmes the network architecture, including the number of node layers, the number of nodes per layer, and the interconnections between nodes. To achieve idle speed control, the neural network processes input signals indicating the current operating state of the engine, including engine speed, the intake mass air flow rate, a desired engine speed, engine temperature, and other variables which influence engine speed, including loads imposed by power steering and air conditioning systems. The network definition data structure holds weight values which determine the manner in which network signals, including the input signals, are combined. The network definition data structures are created by a network training system which utilizes an external training processor which employ dynamic gradient methods to derive network weight values in accordance with a cost function which quantitatively defines system objectives and an identification network which is pretined to provide gradient signals representative of the behavior of the physical plant. The training processor executes training cycles asynchronously with the operation of the EEC module in a representative test vehicle.
摘要翻译： 电子发动机控制（EEC）模块通过控制旁路空气（节气门占空比）和发动机的点火正时来执行神经网络处理程序来控制内燃机的怠速。 神经网络由统一的数据结构定义，该结构限定了网络架构，包括节点层数，每层节点数以及节点间的互连。 为了实现怠速控制，神经网络处理指示发动机的当前操作状态的输入信号，包括发动机速度，进气质量空气流量，期望的发动机转速，发动机温度和影响发动机转速的其它变量，包括负载 由动力转向和空调系统施加。 网络定义数据结构保持重量值，其确定包括输入信号的网络信号的组合方式。 网络定义数据结构由网络训练系统创建，该网络训练系统利用外部训练处理器，该外部训练处理器采用动态梯度方法来根据量化定义系统目标的成本函数和预先提供梯度信号的识别网络来导出网络权重值 代表物理工厂的行为。 训练处理器在代表性测试车辆中与EEC模块的操作异步执行训练周期。
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7.发明授权Method of generation correction tables for misfire detection using neural networks 失效使用神经网络进行失火检测的生成校正表的方法
公开(公告)号：US5774823A
公开(公告)日：1998-06-30
申请号：US923670
申请日：1997-09-04
申请人： John Victor James , Timothy Mark Feldkamp , Kenneth Andrew Marko , Lee Albert Feldkamp , Gintaras Vincent Puskorius
发明人： John Victor James , Timothy Mark Feldkamp , Kenneth Andrew Marko , Lee Albert Feldkamp , Gintaras Vincent Puskorius
CPC分类号： G01M15/11
摘要： A method of automating the calibration of lookup tables containing correction values to be used in an on-board vehicle system is disclosed. The method includes training a neural network to model engine behavior by outputting cylinder specific crankshaft acceleration correction values in response to any engine speed and load input conditions. The correction values generated are stored in a memory device. The training takes place off-board the vehicle, using a data set previously obtained from operating a representative engine under normal operating conditions.
摘要翻译： 公开了一种使包含车载车辆系统中使用的校正值的查找表的校准自动化的方法。 该方法包括训练神经网络以通过响应于任何发动机速度和负载输入条件输出气缸特定的曲轴加速度校正值来模拟发动机行为。 生成的校正值存储在存储器件中。 在正常运行条件下，使用先前从代表性的发动机上获得的数据集进行训练。
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公开(公告)号：US5732382A
公开(公告)日：1998-03-24
申请号：US744258
申请日：1996-11-06
申请人： Gintaras Vincent Puskorius , Lee Albert Feldkamp , Kenneth Andrew Marko , John Victor James , Timothy Mark Feldkamp
发明人： Gintaras Vincent Puskorius , Lee Albert Feldkamp , Kenneth Andrew Marko , John Victor James , Timothy Mark Feldkamp
CPC分类号： G01M15/11 , F02D41/1405
摘要： A method for identifying engine combustion failure of an internal combustion engine having a plurality of cylinders, a crankshaft and a crankshaft position sensor includes the steps of operating the internal combustion engine to rotate the crankshaft, measuring rotational quantities of the crankshaft corresponding to events created by each of the plurality of cylinders during operation of the internal combustion engine, correcting the rotational quantities measured to remove periodic position irregularities to generate a corrected temporal signal, generating an acceleration signal of the crankshaft using the corrected temporal signals, and identifying combustion failures as a function of the acceleration signal. A time-lagged recurrent neural network utilizes the acceleration signal, along with other engine parameters to identify the cylinder-specific misfire events.
摘要翻译： 用于识别具有多个气缸，曲轴和曲轴位置传感器的内燃机的发动机燃烧故障的方法包括以下步骤：操作内燃机以使曲轴旋转，测量曲轴的旋转量，对应于由 在内燃机的运行期间，多个气缸中的每一个，校正测量的旋转量以消除周期性位置不规则以产生校正的时间信号，使用校正的时间信号产生曲轴的加速度信号，以及将燃烧故障识别为 加速度信号的功能。 时间延迟的循环神经网络利用加速度信号以及其他发动机参数来识别气缸特定的失火事件。
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9.发明授权Nonlinear dynamic transform for correction of crankshaft acceleration having torsional oscillations 失效具有扭转振动的曲轴加速度校正的非线性动力学变换
公开(公告)号：US5699253A
公开(公告)日：1997-12-16
申请号：US417361
申请日：1995-04-05
CPC分类号： G01M15/11 , F02D2041/1432 , F02D2200/1012 , F02D2200/1015
摘要： Irregularities in crankshaft velocity introduced when measuring crankshaft rotation at a section of a crankshaft in an internal combustion engine that is less damped to torsional oscillations than is another more accessible crankshaft section are corrected by performing a nonlinear transformation via a neural network to predict rotation measurements that would have been obtained at the inaccessible section from data actually collected at the accessible crankshaft section. Thus, the effects of torsional oscillations in the crankshaft are substantially filtered away, resulting in crankshaft acceleration values that form the basis of a misfire detector having nearly maximum signal-to-noise performance.
摘要翻译： 通过经由神经网络执行非线性变换来校正在比内部燃料发动机曲轴减小扭转振荡的曲轴的曲轴转动时引入曲轴速度的不规则性，以通过神经网络进行非线性变换来预测旋转测量， 将在可访问曲轴部分实际收集的数据的不可接近部分获得。 因此，曲轴中的扭转振荡的影响基本上被过滤掉，导致曲轴加速度值形成具有几乎最大的信噪比性能的失火探测器的基础。
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