Iterative estimation of system parameters using noise-like perturbations
    1.
    发明授权
    Iterative estimation of system parameters using noise-like perturbations 有权
    使用类噪声干扰对系统参数进行迭代估计

    公开(公告)号:US09390065B2

    公开(公告)日:2016-07-12

    申请号:US13949048

    申请日:2013-07-23

    摘要: An estimating computer system may iteratively estimate an unknown parameter of a model or state of a system. An input module may receive numerical data about the system. A noise module may generate random, chaotic, or other type of numerical perturbations of the received numerical data and/or may generate pseudo-random noise. An estimation module may iteratively estimate the unknown parameter of the model or state of the system based on the received numerical data. The estimation module may use the numerical perturbations and/or the pseudo-random noise and the input numerical data during at least one of the iterative estimates of the unknown parameter. A signaling module may signal when successive parameter estimates or information derived from successive parameter estimates differ by less than a predetermined signaling threshold or when the number of estimation iterations reaches a predetermined number.

    摘要翻译: 估计计算机系统可以迭代地估计系统的模型或状态的未知参数。 输入模块可以接收关于系统的数字数据。 噪声模块可能产生接收的数值数据的随机,混沌或其他类型的数字扰动和/或可能产生伪随机噪声。 估计模块可以基于接收到的数值数据迭代地估计系统的模型或状态的未知参数。 在未知参数的迭代估计中的至少一个期间,估计模块可以使用数字扰动和/或伪随机噪声和输入数值数据。 当连续参数估计或从连续参数估计导出的信息差异小于预定信令阈值时或当估计迭代次数达到预定数量时,信令模块可以发信号。

    NOISE-BOOSTED BACK PROPAGATION AND DEEP LEARNING NEURAL NETWORKS
    2.
    发明申请
    NOISE-BOOSTED BACK PROPAGATION AND DEEP LEARNING NEURAL NETWORKS 审中-公开
    噪声增强背景传播和深度学习神经网络

    公开(公告)号:US20160034814A1

    公开(公告)日:2016-02-04

    申请号:US14816999

    申请日:2015-08-03

    IPC分类号: G06N3/08 G06N99/00 G06N3/04

    CPC分类号: G06N3/08

    摘要: A learning computer system may update parameters and states of an uncertain system. The system may receive data from a user or other source; process the received data through layers of processing units, thereby generating processed data; process the processed data to produce one or more intermediate or output signals; compare the one or more intermediate or output signals with one or more reference signals to generate information indicative of a performance measure of one or more of the layers of processing units; send information indicative of the performance measure back through the layers of processing units; process the information indicative of the performance measure in the processing units and in interconnections between the processing units; generate random, chaotic, fuzzy, or other numerical perturbations of the received data, the processed data, or the one or more intermediate or output signals; update the parameters and states of the uncertain system using the received data, the numerical perturbations, and previous parameters and states of the uncertain system; determine whether the generated numerical perturbations satisfy a condition; and if the numerical perturbations satisfy the condition, inject the numerical perturbations into one or more of the parameters or states, the received data, the processed data, or one or more of the processing units.

    摘要翻译: 学习计算机系统可以更新不确定系统的参数和状态。 系统可以从用户或其他来源接收数据; 通过处理单元层处理接收到的数据,从而生成处理的数据; 处理经处理的数据以产生一个或多个中间或输出信号; 将一个或多个中间或输出信号与一个或多个参考信号进行比较以产生指示处理单元的一个或多个层的性能测量的信息; 通过处理单元的层发送指示性能测量的信息; 处理指示处理单元中的性能测量的信息以及处理单元之间的互连; 产生接收数据,处理数据或一个或多个中间或输出信号的随机,混沌,模糊或其他数字扰动; 使用接收到的数据,数值扰动和不确定系统的先前参数和状态来更新不确定系统的参数和状态; 确定所产生的数字扰动是否满足条件; 并且如果数字扰动满足条件,则将数字扰动注入一个或多个参数或状态,接收的数据,处理的数据或一个或多个处理单元。

    NOISE SPEED-UPS IN HIDDEN MARKOV MODELS WITH APPLICATIONS TO SPEECH RECOGNITION
    3.
    发明申请
    NOISE SPEED-UPS IN HIDDEN MARKOV MODELS WITH APPLICATIONS TO SPEECH RECOGNITION 审中-公开
    噪音速度型UPS用于语音识别应用

    公开(公告)号:US20160005399A1

    公开(公告)日:2016-01-07

    申请号:US14802760

    申请日:2015-07-17

    摘要: A learning computer system may estimate unknown parameters and states of a stochastic or uncertain system having a probability structure. The system may include a data processing system that may include a hardware processor that has a configuration that: receives data; generates random, chaotic, fuzzy, or other numerical perturbations of the data, one or more of the states, or the probability structure; estimates observed and hidden states of the stochastic or uncertain system using the data, the generated perturbations, previous states of the stochastic or uncertain system, or estimated states of the stochastic or uncertain system; and causes perturbations or independent noise to be injected into the data, the states, or the stochastic or uncertain system so as to speed up training or learning of the probability structure and of the system parameters or the states.

    摘要翻译: 学习计算机系统可以估计具有概率结构的随机或不确定系统的未知参数和状态。 该系统可以包括数据处理系统,其可以包括具有以下配置的硬件处理器:接收数据; 产生数据的随机,混乱,模糊或其他数字扰动,一个或多个状态或概率结构; 使用数据,生成的扰动,随机或不确定系统的先前状态或随机或不确定系统的估计状态的随机或不确定系统的观测和隐藏状态的估计; 并引起扰动或独立噪声注入到数据,状态或随机或不确定系统中,以加速对概率结构和系统参数或状态的训练或学习。

    ITERATIVE ESTIMATION OF SYSTEM PARAMETERS USING NOISE-LIKE PERTURBATIONS
    4.
    发明申请
    ITERATIVE ESTIMATION OF SYSTEM PARAMETERS USING NOISE-LIKE PERTURBATIONS 有权
    使用噪声类似扰动的系统参数的迭代估计

    公开(公告)号:US20140025356A1

    公开(公告)日:2014-01-23

    申请号:US13949048

    申请日:2013-07-23

    IPC分类号: G06F17/10 G06F17/18

    摘要: An estimating computer system may iteratively estimate an unknown parameter of a model or state of a system. An input module may receive numerical data about the system. A noise module may generate random, chaotic, or other type of numerical perturbations of the received numerical data and/or may generate pseudo-random noise. An estimation module may iteratively estimate the unknown parameter of the model or state of the system based on the received numerical data. The estimation module may use the numerical perturbations and/or the pseudo-random noise and the input numerical data during at least one of the iterative estimates of the unknown parameter. A signaling module may signal when successive parameter estimates or information derived from successive parameter estimates differ by less than a predetermined signaling threshold or when the number of estimation iterations reaches a predetermined number.

    摘要翻译: 估计计算机系统可以迭代地估计系统的模型或状态的未知参数。 输入模块可以接收关于系统的数字数据。 噪声模块可能产生接收的数值数据的随机,混沌或其他类型的数字扰动和/或可能产生伪随机噪声。 估计模块可以基于接收到的数值数据迭代地估计系统的模型或状态的未知参数。 在未知参数的迭代估计中的至少一个期间,估计模块可以使用数字扰动和/或伪随机噪声和输入数值数据。 当连续参数估计或从连续参数估计导出的信息差异小于预定信令阈值时或当估计迭代次数达到预定数量时,信令模块可以发信号。

    NOISE-ENHANCED CONVOLUTIONAL NEURAL NETWORKS
    5.
    发明申请
    NOISE-ENHANCED CONVOLUTIONAL NEURAL NETWORKS 审中-公开
    噪声增强的神经网络神经网络

    公开(公告)号:US20160019459A1

    公开(公告)日:2016-01-21

    申请号:US14803797

    申请日:2015-07-20

    IPC分类号: G06N3/08 G06N3/04

    摘要: A learning computer system may include a data processing system and a hardware processor and may estimate parameters and states of a stochastic or uncertain system. The system may receive data from a user or other source; process the received data through layers of processing units, thereby generating processed data; apply masks or filters to the processed data using convolutional processing; process the masked or filtered data to produce one or more intermediate and output signals; compare the output signals with reference signals to generate error signals; send and process the error signals back through the layers of processing units; generate random, chaotic, fuzzy, or other numerical perturbations of the received data, the processed data, or the output signals; estimate the parameters and states of the stochastic or uncertain system using the received data, the numerical perturbations, and previous parameters and states of the stochastic or uncertain system; determine whether the generated numerical perturbations satisfy a condition; and, if the numerical perturbations satisfy the condition, inject the numerical perturbations into the estimated parameters or states, the received data, the processed data, the masked or filtered data, or the processing units.

    摘要翻译: 学习计算机系统可以包括数据处理系统和硬件处理器,并且可以估计随机或不确定系统的参数和状态。 系统可以从用户或其他来源接收数据; 通过处理单元层处理接收到的数据,从而生成处理的数据; 使用卷积处理对已处理数据应用掩码或过滤器; 处理屏蔽或滤波的数据以产生一个或多个中间和输出信号; 将输出信号与参考信号进行比较,产生误差信号; 通过处理单元的层发送和处理错误信号; 产生接收数据,处理数据或输出信号的随机,混沌,模糊或其他数字扰动; 使用接收数据,数值扰动以及随机或不确定系统的先前参数和状态来估计随机或不确定系统的参数和状态; 确定所产生的数字扰动是否满足条件; 并且如果数值扰动满足条件,则将数值扰动注入估计的参数或状态,接收的数据,处理的数据,被掩蔽的或被滤波的数据或处理单元。

    NOISE-ENHANCED CLUSTERING AND COMPETITIVE LEARNING
    6.
    发明申请
    NOISE-ENHANCED CLUSTERING AND COMPETITIVE LEARNING 审中-公开
    噪声增强和竞争性学习

    公开(公告)号:US20150161232A1

    公开(公告)日:2015-06-11

    申请号:US14553890

    申请日:2014-11-25

    摘要: Non-transitory, tangible, computer-readable storage media may contain a program of instructions that enhances the performance of a computing system running the program of instructions when segregating a set of data into subsets that each have at least one similar characteristic. The instructions may cause the computer system to perform operations comprising: receiving the set of data; applying an iterative clustering algorithm to the set of data that segregates the data into the subsets in iterative steps; during the iterative steps, injecting perturbations into the data that have an average magnitude that decreases during the iterative steps; and outputting information identifying the subsets.

    摘要翻译: 非暂时的,有形的,计算机可读的存储介质可以包含指令程序,当将一组数据分离成各自具有至少一个类似特征的子集时,该指令程序增强了运行指令程序的计算系统的性能。 所述指令可能导致所述计算机系统执行操作,包括:接收所述数据集; 将迭代聚类算法应用于以迭代步骤将数据分离成子集的数据集; 在迭代步骤期间,将扰动注入具有在迭代步骤期间减小的平均幅度的数据; 并输出识别子集的信息。