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首页> 外文期刊>Engineering Applications of Artificial Intelligence >Novel method and system for pattern recognition and processing using data encoded as Fourier series in Fourier space
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Novel method and system for pattern recognition and processing using data encoded as Fourier series in Fourier space

机译：在傅立叶空间中使用编码为傅立叶级数的数据进行模式识别和处理的新方法和系统

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摘要

A method and system for pattern recognition and processing is reported that has a data structure and theoretical basis that are unique. This novel approach anticipates the signal processing action of an ensemble of neurons as a unit and intends to simulate aspects of brain that give rise to capabilities such as intelligence, pattern recognition, and reasoning that have not been reproduced with past approaches such as neural networks that are based individual simulated "neuronal units." Information representative of physical characteristics or representations of physical characteristics is transformed into a Fourier series in Fourier space within an input context of the physical characteristics that is encoded in time as delays corresponding to modulation of the Fourier series at corresponding frequencies. Associations are formed between Fourier series by filtering the Fourier series and by using a spectral similarity between the filtered Fourier series to determine the association based on Poissonian probability. The associated Fourier series are added to form strings of Fourier series. Each string is ordered by filtering it with multiple selected filters to form multiple time order formatted subset Fourier series, and by establishing the order through associations with one or more initially ordered strings to form an ordered string. Associations are formed between the ordered strings to form complex ordered strings that relate similar items of interest. The components of the system based on the algorithm are active based on probability using weighting factors based on activation rates.
机译：报告了一种用于模式识别和处理的方法和系统，其具有独特的数据结构和理论基础。这种新颖的方法将神经元整体的信号处理功能作为一个单元进行了预测，并旨在模拟大脑的某些方面，这些方面会引起诸如智能，模式识别和推理之类的功能，而这些功能是过去的方法（如神经网络）无法重现的。是基于单个模拟的“神经元单位”。表示物理特征的信息或物理特征的表示在物理特征的输入上下文内在傅立叶空间中转换为傅立叶级数，该输入上下文在时间上被编码为与在相应频率下对傅立叶级数的调制相对应的延迟。通过对傅立叶级数进行滤波并通过使用滤波后的傅立叶级数之间的频谱相似性来确定傅立叶级数之间的关联，从而基于泊松概率确定关联。添加了相关的傅立叶级数以形成傅立叶级数的字符串。通过使用多个选定的过滤器对每个字符串进行过滤来排序，以形成多个时间顺序格式的子集傅里叶级数，并通过与一个或多个初始排序的字符串的关联来建立顺序，从而形成一个排序的字符串，从而对每个字符串进行排序。在有序字符串之间形成关联以形成复杂的有序字符串，这些复杂的有序字符串关联了所关注的相似项。基于算法的系统组件基于概率，使用基于激活率的加权因子进行激活。

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