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    • 5. 发明授权
    • Neural network apparatus
    • 神经网络装置
    • US5283838A
    • 1994-02-01
    • US685596
    • 1991-04-15
    • Fumio TogawaToru UedaTakashi AramakiYasushi Ishizuka
    • Fumio TogawaToru UedaTakashi AramakiYasushi Ishizuka
    • G06G7/60G06F15/18G06K9/66G06N3/08G06N3/10G06N99/00G11C11/54G06K9/00
    • G06K9/6272G06N3/10
    • When performing learning for a neural network, a plurality of learning vectors which belong to an arbitrary category are used, and self-organization learning in the category is carried out. As a result, the plurality of learning vectors which belong to the category are automatically clustered, and the contents of weight vectors in the neural network are set to representative vectors which exhibit common features of the learning vectors of each cluster. Then, teacher-supervised learning is carried out for the neural network, using the thus set contents of the weight vectors as initial values thereof. In the learning process, an initial value of each weight vector is set to the representative vector of each cluster obtained by clustering. Therefore, the number of calculations required until the teacher-supervised learning is converged is greatly reduced.
    • 当对神经网络进行学习时,使用属于任意类别的多个学习向量,并且执行该类别中的自组织学习。 结果,属于该类别的多个学习向量被自动聚类,将神经网络中权重向量的内容设置为表示每个簇的学习向量的共同特征的代表向量。 然后,对神经网络进行教师监督学习,使用这样设定的权重向量的内容作为其初始值。 在学习过程中,将每个权重向量的初始值设置为通过聚类获得的每个簇的代表向量。 因此,在教师监督学习融合之前所需的计算量大大减少。