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31 条记录 (耗时 0.038 秒)

    Hairdressing preparation
    5.
    发明授权
    Hairdressing preparation 失效
    理发准备

    公开(公告)号：US4151269A

    公开(公告)日：1979-04-24

    申请号：US786540

    申请日：1977-04-11

    申请人： Kenji Torii ,  Kenichi Tomita

    发明人： Kenji Torii ,  Kenichi Tomita

    IPC分类号： A61K8/86 ,  A61Q5/06 ,  A61K7/06

    CPC分类号： A61K8/86 ,  A61Q5/06

    摘要： A hairdressing preparation comprising at least one polyether compound resulting from the addition polymerization of 20 to 90 moles of propylene oxide to 1 mole of a polyhydric alcohol to form a main chain, and the subsequent addition polymerization of 1 to 10 moles of ethylene oxide to the resulting main chain, the amount of ethylene oxide being further within the range of 1 to 10% by weight based on the total amount of propylene oxide and ethylene oxide. The hairdressing preparation has good dressability, good feeling in use, good washability from the hair and wearing apparel and extremely low eye irritation and toxicity.

    摘要翻译： 一种理发制剂，其包含至少一种由20至90摩尔环氧丙烷加成聚合得到的聚醚化合物与1摩尔多元醇形成主链，然后将1至10摩尔环氧乙烷加成到 基于环氧丙烷和环氧乙烷的总量，环氧乙烷的量进一步在1至10重量％的范围内。 美发制剂具有良好的可穿戴性，良好的使用感，从头发和穿着的服装具有良好的洗涤性和极低的眼睛刺激和毒性。

    System for building an artificial neural network
    7.
    发明授权
    System for building an artificial neural network 失效
    构建人工神经网络的系统

    公开(公告)号：US6049793A

    公开(公告)日：2000-04-11

    申请号：US970050

    申请日：1997-11-13

    申请人： Kenichi Tomita

    发明人： Kenichi Tomita

    IPC分类号： G06N3/08 ,  G06N3/10 ,  G06N3/02 ,  G06N5/00

    CPC分类号： G06K9/6253 ,  G06K9/6281 ,  G06N3/08 ,  G06N3/105

    摘要： A system for building an artificial neural network is provided which precisely defines the network's structure of artificial neurons, and non-iteratively determines the synapse-weights and hard limiter threshold of each artificial neuron of the network. The system includes a computer for analyzing input data, which represents patterns of different classes of signals, to generate one or more data points in two or three dimensions representative of the signals in each of the different classes. A distribution of the data points is visualized on a map on an output device coupled to the computer. The data points are clustered on the map into clusters in accordance with the classes associated with the data points, and the map is then partitioned into regions by defining linear boundaries between clusters. The artificial neural network is configured in accordance with the data points, clusters, boundaries, and regions, such that each boundary represents a different artificial neuron of the artificial neural network, and the geometric relationship of the regions on the map to the classes defines the logic connectivity of the artificial neurons. The synaptic weights and threshold of each artificial neuron in the network are graphically determined based on the data points of the map.

    摘要翻译： 提供了一种用于构建人造神经网络的系统，其精确地定义了人造神经元的网络结构，并且不迭代地确定网络的每个人造神经元的突触权重和硬限制器阈值。 该系统包括用于分析表示不同类别信号的模式的输入数据的计算机，以生成表示每个不同类别中的信号的二维或三维的一个或多个数据点。 在耦合到计算机的输出设备上的地图上可视化数据点的分布。 数据点根据与数据点相关联的类在地图上聚类成簇，然后通过定义簇之间的线性边界将地图划分成多个区域。 人造神经网络根据数据点，簇，边界和区域进行配置，使得每个边界表示人造神经网络的不同人造神经元，并且地图上的区域与类别之间的几何关系定义为 人造神经元的逻辑连通性。 网络中每个人造神经元的突触权重和阈值是基于地图的数据点进行图形确定的。