公开(公告)号：US20160005399A1

公开(公告)日：2016-01-07

申请号：US14802760

申请日：2015-07-17

申请人： Kartik Audhkhasi ,  Osonde Osoba ,  Bart Kosko

发明人： Kartik Audhkhasi ,  Osonde Osoba ,  Bart Kosko

IPC分类号： G10L15/14 ,  G10L25/33 ,  G10L25/36 ,  G10L15/06

CPC分类号： G10L15/144 ,  G10L15/063 ,  G10L25/33 ,  G10L25/36

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。

公开(公告)号：US11495213B2

公开(公告)日：2022-11-08

申请号：US14802760

申请日：2015-07-17

申请人： UNIVERSITY OF SOUTHERN CALIFORNIA

发明人： Kartik Audhkhasi ,  Osonde Osoba ,  Bart Kosko

IPC分类号： G10L25/36 ,  G10L15/14 ,  G10L15/06 ,  G10L25/33

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.

公开(公告)号：US5768437A

公开(公告)日：1998-06-16

申请号：US295637

申请日：1994-08-26

申请人： Donald Martin Monro ,  Frank Dudbridge

发明人： Donald Martin Monro ,  Frank Dudbridge

IPC分类号： H04N7/26 ,  G06T9/00 ,  G10L25/36 ,  H03M7/30 ,  H04N1/41 ,  G06K9/36 ,  G06K9/46

CPC分类号： H04N19/99 ,  G06T9/001 ,  G10L25/36

摘要： A method of fractal coding of data and apparatus therefor, which method comprises dividing data into domains, determining a set of transformations relating the domains to the data in such a manner as to minimize error between the data and an approximation to the data obtained by application of the transformation, and providing an expression of a series of quantized fractal coefficients characterizing the transformations. A transformation includes at least one part indicating a domain and at least another (functional) part indicating a value for a measure associatable with a specific domain or aspect thereof.

摘要翻译： PCT No.PCT / GB93 / 00422 Sec。 371日期1994年8月26日 102（e）日期1994年8月26日PCT 1993年3月1日PCT公布。 公开号WO93 / 17519 日期1993年9月2日一种数据及其装置的分形编码方法，该方法包括将数据划分成域，确定一组与数据相关的变换，以使数据之间的误差最小化， 通过应用变换获得的数据，并提供表征变换的一系列量化分形系数的表达式。 变换包括指示域的至少一个部分和指示与特定域或其方面相关联的度量的值的至少另一（功能）部分。