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    • 3. 发明授权
    • Pattern recognition method using fuzzy neuron
    • 模糊识别方法使用模糊神经元
    • US5592564A
    • 1997-01-07
    • US429634
    • 1995-04-27
    • Fumiaki ShigeokaMasanari Oh
    • Fumiaki ShigeokaMasanari Oh
    • G06F9/44G06F15/18G06G7/12G06G7/60G06K9/46G06K9/66G06K9/68G06N3/04G06N7/02G06N99/00G06T7/00G06K9/62
    • G06K9/4623G06N3/0436
    • A method for determining whether a fuzzy symbol matches a predetermined reference pattern by generating membership functions that collectively represent a reference pattern having identifying features; sampling the fuzzy symbol to generate an input pattern representative of the fuzzy symbol; transforming the input pattern to generate an inverted input pattern; comparing the input pattern with a first membership function to determine a first quantity of identifying features of the reference pattern that are present in the fuzzy symbol; comparing the inverted input pattern with a second membership function to determine a second quantity of identifying features of the reference pattern that are present in the fuzzy symbol; and determining that the fuzzy symbol matches the reference pattern if the first and second quantities are sufficiently high.
    • 一种用于通过产生共同表示具有识别特征的参考图案的隶属函数来确定模糊符号是否匹配预定参考图案的方法; 对模糊符号进行采样以产生代表模糊符号的输入模式; 变换输入图案以产生反相输入图案; 将输入模式与第一隶属度函数进行比较,以确定存在于模糊符号中的参考模式的识别特征的第一数量; 将所述反相输入模式与第二隶属函数进行比较,以确定存在于所述模糊符号中的参考模式的识别特征的第二数量; 以及如果所述第一和第二数量足够高,则确定所述模糊符号与所述参考图案匹配。
    • 6. 发明授权
    • Fuzzy neuron for patter recognition
    • 用于模式识别的模糊神经元
    • US5434930A
    • 1995-07-18
    • US160274
    • 1993-12-02
    • Fumiaki ShigeokaMasanari Oh
    • Fumiaki ShigeokaMasanari Oh
    • G06F9/44G06F15/18G06G7/12G06G7/60G06K9/46G06K9/66G06K9/68G06N3/04G06N7/02G06N99/00G06T7/00G06K9/62
    • G06K9/4623G06N3/0436
    • A pattern recognition method capable of implementing a system suitable for the recognition of a complicated pattern. A reference pattern to be collated with an input pattern is represented using a plurality of membership functions to thereby apply fuzziness to the reference pattern. The input pattern which is input along a cross-detecting line is logically inverted for the production of an inverted input pattern. The input pattern is collated with any one of the plurality of membership functions to judge whether or not the input pattern is provided with at least one of the features of the reference pattern. Also, the inverted input pattern is collated with any one of the other membership functions to thereby judge whether or not the input pattern is provided with at least another one of the features of the reference pattern. The results of these inversions are synthesized. If the obtained result indicates that the input pattern sufficiently matches the reference pattern, then the input pattern is recognized to be a pattern belonging to the same category as the reference pattern.
    • 一种能够实现适合于识别复杂图案的系统的图案识别方法。 使用多个隶属函数来表示与输入图案对照的参考图案,从而将模糊性应用于参考图案。 沿着交叉检测线输入的输入图案在逻辑上反转以产生反相输入图案。 将输入图案与多个隶属函数中的任何一个进行核对,以判断输入图案是否具有参考图案的特征中的至少一个。 此外,将反相输入图案与其他隶属函数中的任何一个进行核对,从而判断输入图案是否具有参考图案的特征中的至少另一个。 这些反转的结果是合成的。 如果获得的结果指示输入图案与参考图案充分匹配,则输入图案被识别为属于与参考图案相同类别的图案。