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1. 发明申请

WO2002086814A1 AN ON-LINE METHOD FOR SELF-ORGANIZED LEARNING AND EXTRACTION OF FUZZY RULES 审中-公开
标题翻译：自组织学习和提取模糊规则的在线方法
公开(公告)号：WO2002086814A1
公开(公告)日：2002-10-31
申请号：PCT/US2002/013069
申请日：2002-04-23
申请人： HRL LABORATORIES, LLC , SRINIVASA, Narayan , MEDASANI, Swarup, S.
发明人： SRINIVASA, Narayan , MEDASANI, Swarup, S.
IPC分类号： G06N7/02
CPC分类号： G06N7/023 , G06K9/00362
摘要： The present invention provides an improved method and system for training an on-line fuzzy interference network to generate a rule base, and a rule base generated thereby. Turning and applying a learning rule to the fuzzy rules generated by the fuzzy interference network in such a manner as to divorce the performance of the network from the number of input dimension allows the present invention to adapt a fuzzy inference network such as a SONFIN to be effective for the classification of high-dimensional data in problems requiring the use of a high number of dimensions such as occupant recognition in vehicles, weather forecasting, and economic forecasting.
摘要翻译：本发明提供了一种用于训练在线模糊干扰网络以生成规则库的改进方法和系统，以及由此产生的规则库。将模糊干扰网络所产生的模糊规则转换和应用于使得网络的性能与输入维数的离散的方式允许本发明使诸如SONFIN之类的模糊推理网络适应于有效地在需要使用诸如车辆中的乘客识别，天气预报和经济预测等大量维度的问题中对高维数据进行分类。

2. 发明申请

WO2012166821A3 SPIKE DOMAIN CIRCUIT AND MODELING METHOD 审中-公开
标题翻译： SPIKE域电路和建模方法
公开(公告)号：WO2012166821A3
公开(公告)日：2013-03-07
申请号：PCT/US2012040043
申请日：2012-05-30
申请人： HRL LAB LLC , CRUZ-ALBRECHT JOSE , PETRE PETER , SRINIVASA NARAYAN
发明人： CRUZ-ALBRECHT JOSE , PETRE PETER , SRINIVASA NARAYAN
IPC分类号： H03M3/00
CPC分类号： G06N3/049 , G06N3/063 , H03M3/43 , H03M3/454
摘要： A spike domain circuit responsive to analog and/or spike domain input signals. Has a hysteresis quantizer for generating a spike domain output signal z(t); a one bit DAC having an input coupled to receive the spike domain output signal z(t) and an output coupled to a current summing node; and a second order filter stage having two inputs, one coupled to receive the spike domain output signal z(t); the other coupled to receive current summed at said current summing node. The second order filter stage has an output coupled to an input of the hysteresis quantizer. The current summing node also receives signals related to the analog and/or spike domain input signals for response. The circuit may serve as a neural node. Many such circuits may be used together to model neurons with complex biological dynamics.
摘要翻译：响应于模拟和/或尖峰域输入信号的尖峰域电路。具有用于产生尖峰域输出信号z（t）的滞后量化器; 具有耦合以接收尖峰域输出信号z（t）的输入和耦合到电流求和节点的输出的一比特DAC; 和具有两个输入的二阶滤波器级，一个被耦合以接收尖峰域输出信号z（t）; 另一个耦合以接收在所述当前求和节点处相加的电流。二阶滤波器级具有耦合到迟滞量化器的输入的输出。当前求和节点还接收与模拟和/或尖峰域输入信号有关的信号以作出响应。该电路可以用作神经节点。许多这样的电路可以一起使用以用复杂的生物动力学来模拟神经元。

3. 发明申请

WO2012166821A2 SPIKE DOMAIN CIRCUIT AND MODELING METHOD 审中-公开
标题翻译： SPIKE域电路和建模方法
公开(公告)号：WO2012166821A2
公开(公告)日：2012-12-06
申请号：PCT/US2012/040043
申请日：2012-05-30
申请人： HRL LABORATORIES, LLC , CRUZ-ALBRECHT, Jose , PETRE, Peter , SRINIVASA, Narayan
发明人： CRUZ-ALBRECHT, Jose , PETRE, Peter , SRINIVASA, Narayan
IPC分类号： H03M3/00
CPC分类号： G06N3/049 , G06N3/063 , H03M3/43 , H03M3/454
摘要： A spike domain circuit responsive to analog and/or spike domain input signals. Has a hysteresis quantizer for generating a spike domain output signal z(t); a one bit DAC having an input coupled to receive the spike domain output signal z(t) and an output coupled to a current summing node; and a second order filter stage having two inputs, one coupled to receive the spike domain output signal z(t); the other coupled to receive current summed at said current summing node. The second order filter stage has an output coupled to an input of the hysteresis quantizer. The current summing node also receives signals related to the analog and/or spike domain input signals for response. The circuit may serve as a neural node. Many such circuits may be used together to model neurons with complex biological dynamics.
摘要翻译：响应于模拟和/或尖峰域输入信号的尖峰域电路。具有用于产生尖峰域输出信号z（t）的滞后量化器; 具有耦合以接收尖峰域输出信号z（t）的输入和耦合到电流求和节点的输出的一比特DAC; 和具有两个输入的二阶滤波器级，一个被耦合以接收尖峰域输出信号z（t）; 另一个耦合以接收在所述当前求和节点处相加的电流。二阶滤波器级具有耦合到迟滞量化器的输入的输出。当前求和节点还接收与模拟和/或尖峰域输入信号有关的信号以作出响应。该电路可以用作神经节点。许多这样的电路可以一起使用以用复杂的生物动力学来模拟神经元。

4. 发明申请

WO0161647A3 FUZZY EXPERT SYSTEM FOR INTERPRETABLE RULE EXTRACTION FROM NEURAL NETWORKS 审中-公开
标题翻译： FUZZY专家系统，用于从神经网络中提取可解释的规则
公开(公告)号：WO0161647A3
公开(公告)日：2002-05-10
申请号：PCT/US0101629
申请日：2001-01-18
申请人： HRL LAB LLC , SRINIVASA NARAYAN , OWECHKO YURI
发明人： SRINIVASA NARAYAN , OWECHKO YURI
IPC分类号： G06K9/00 , G06K9/66 , G06N3/04 , G06N7/04
CPC分类号： G06K9/626 , G06K9/00369 , G06K9/00832 , G06N3/0427 , G06N7/046
摘要： A method and apparatus for extracting an interpretable, meaningful, and concise rules set from neural networks is presented. The method involves adjustement of gain parameter, lambda and the threshold, T j for the sigmoid activation function of the interactive-or operator used in the extraction/development of a rule set from an artificial neural network. A multi-stage procedure involving coarse (500) and fine adjustment (514) is used in order to constrain the range of the antecedents of the extracted rules to the range of values of the inputs to the artificial neural network. Furthermore, the consequents of the extracted rules are provided based on degree of membership such that they are easily understandable by human beings. The method disclosed may be applied to any pattern recognition task, and is particularly useful in applications such as vehicle occupant sensing and recognition, object recognition, gesture recognition, and facial pattern recognition, among others.
摘要翻译：提出了一种用于从神经网络中提取可解释，有意义和简明的规则集的方法和装置。该方法涉及对从用于人造神经网络的规则集的提取/开发中使用的交互式或运算符的S形激活函数的增益参数，λ和阈值的调整。使用涉及粗略（500）和精细调整（514）的多阶段过程，以便将提取的规则的前提的范围限制到人造神经网络的输入值的范围。此外，提取的规则的结果是基于成员的程度提供，使得它们容易被人理解。所公开的方法可以应用于任何模式识别任务，并且在诸如车辆乘员感测和识别，对象识别，手势识别和面部模式识别等应用中特别有用。

5. 发明申请

WO2016154440A1 SPARSE INFERENCE MODULES FOR DEEP LEARNING 审中-公开
标题翻译：用于深度学习的稀疏参数模块
公开(公告)号：WO2016154440A1
公开(公告)日：2016-09-29
申请号：PCT/US2016/024017
申请日：2016-03-24
申请人： HRL LABORATORIES, LLC , PILLY, Praveen, K. , STEPP, Nigel, D. , SRINIVASA, Narayan
发明人： PILLY, Praveen, K. , STEPP, Nigel, D. , SRINIVASA, Narayan
IPC分类号： G06N5/04 , G06N3/08 , G06F15/18 , G06F17/00
CPC分类号： G06N3/08 , G06F15/18 , G06K9/4628 , G06K9/6249 , G06K9/6272 , G06K9/64 , G06N3/0454 , G06N3/0481 , G06N5/04
摘要： Described is a sparse inference module that can be incorporated into a deep learning •system. For example, the deep learning system includes a plurality of hierarchical feature channel layers, each feature channel layer having a set of filters. A plurality of sparse inference modules can be included, such that a sparse inference module resides electronically within each feature channel layer. Each sparse inference module is configured to receive data and match the data against a plurality of pattern templates to generate a. degree of match value for each of the pattern templates, wife the degree of match values being sparsified such that only those degree of match values that exceed a predetermined threshold, or a fixed number of the top degree of match values, are provided to subsequent feature, channels in the plurality of hierarchical feature channels, while other, losing degree of match values are quenched to zero.
摘要翻译：描述了一个稀疏的推理模块，可以并入深入学习系统。例如，深度学习系统包括多个分层特征信道层，每个特征信道层具有一组过滤器。可以包括多个稀疏推理模块，使得稀疏推理模块电子地驻留在每个特征通道层内。每个稀疏推理模块被配置为接收数据并使数据与多个模式模板匹配以产生a。每个模式模板的匹配值的程度，妻子将匹配值的程度进行稀疏化，使得只有超过预定阈值的匹配值的程度或最大匹配值的固定数量被提供给随后的特征，多个分层特征通道中的通道，而另一些失去匹配值的程度被淬灭到零。

6. 发明申请

WO2015073713A1 SYSTEM FOR CONTROLLING BRAIN MACHINE INTERFACES AND NEURAL PROSTHETIC SYSTEMS 审中-公开
标题翻译：控制脑机接口和神经系统的系统
公开(公告)号：WO2015073713A1
公开(公告)日：2015-05-21
申请号：PCT/US2014/065537
申请日：2014-11-13
申请人： HRL LABORATORIES, LLC , DE SAPIO, Vincent , SRINIVASA, Narayan
发明人： DE SAPIO, Vincent , SRINIVASA, Narayan
IPC分类号： A61F2/68 , A61F2/72
CPC分类号： A61F2/68 , A61F2/72 , A61F2002/701 , A61F2002/7615 , G06N3/049
摘要： Described is a system for controlling a torque controlled prosthetic device given motor intent inferred from neuroimaging data. The system includes at least one torque controlled prosthetic device operably connected with one or more processors. Further, the system is configured to receive neuroimaging data of a user from a neuroimaging device and decode the neuroimaging data to infer spatial motion intent of the user, where the spatial motion intent includes desired motion commands of the torque controlled prosthetic device represented in a coordinate system. The system thereafter executes, with a prosthesis controller, the motion commands as torque commands to cause the torque controlled prosthetic device to move according to the spatial motion intent of the user.
摘要翻译：描述了一种用于控制由神经成像数据推断的给定电机意图的扭矩控制假体装置的系统。该系统包括与一个或多个处理器可操作地连接的至少一个扭矩控制的假体装置。此外，该系统被配置为从神经成像设备接收用户的神经成像数据并且解码神经成像数据以推断用户的空间运动意图，其中空间运动意图包括在坐标中表示的扭矩控制的假体装置的期望运动命令系统。该系统随后用假体控制器执行运动命令作为扭矩命令，以使扭矩控制的假体装置根据使用者的空间运动意图而移动。

7. 发明申请

WO2003091941A1 HIGH-PERFORMANCE OBJECT DETECTION WITH IMAGE DATA FUSION 审中-公开
标题翻译：高性能对象检测与图像数据融合
公开(公告)号：WO2003091941A1
公开(公告)日：2003-11-06
申请号：PCT/US2003/012714
申请日：2003-04-24
申请人： HRL LABORATORIES , OWECHKO, Yuri , SRINIVASA, Narayan , MEDASANI, Swarup , BOSCOLO, Riccardo
发明人： OWECHKO, Yuri , SRINIVASA, Narayan , MEDASANI, Swarup , BOSCOLO, Riccardo
IPC分类号： G06K9/00
CPC分类号： G06K9/6292 , G06K9/00201 , G06K9/00369 , G06K9/00832 , G06K9/209 , G06K9/522 , G06T7/97 , G06T2207/10021 , G06T2207/20064 , G06T2207/30268 , H04N2013/0081 , H04N2013/0092
摘要： A vision-based system for automatically detecting the type of object within a specified area, such as the type of occupant within a vehicle is presented. The type of occupant can then be used to determine whether an airbag deployment system should be enabled or not. The system extracts different features, including wavelet features and/or a disparity map from images captured by image sensors. These features are then processed by classification algorithms to produce class confidences for various occupant types. The occupant class confidences are fused and processed to determine occupant type. In a preferred embodiment, image features from image edges, wavelet features, and disparity are used. Various classification algorithms may be implemented to classify the object. Use of the disparity map and/or wavelet features provides greater computational efficiency.
摘要翻译：提出了一种基于视觉的系统，用于自动检测指定区域内物体的类型，例如车辆内乘客的类型。然后可以使用乘客的类型来确定是否应启用安全气囊展开系统。该系统从图像传感器捕获的图像中提取不同的特征，包括小波特征和/或视差图。然后通过分类算法对这些特征进行处理，以便为各种乘客类型生成类别信息。乘员班信心被融合和处理以确定乘客类型。在优选实施例中，使用来自图像边缘的图像特征，小波特征和视差。可以实现各种分类算法来对对象进行分类。使用视差图和/或小波特征提供更大的计算效率。

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