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    • 32. 发明申请
    • BATTERY CHARGER AND METHOD THEREFOR
    • 电池充电器及其方法
    • US20070229037A1
    • 2007-10-04
    • US11759738
    • 2007-06-07
    • Isao Hayashi
    • Isao Hayashi
    • H02J7/00
    • H02J7/042H02J7/0055
    • To fully charge a battery by a multi-power-source battery charger either when a DC power having a voltage corresponding to the charging voltage of the battery is input or when a DC power having a voltage higher than the charging voltage of the battery is input, the supply destination of the input DC power is switched, in accordance with the voltage of the DC power input to the battery charger, between a controller which controls charging of the battery in accordance with the charging voltage of the battery and a DC/DC converter which controls the voltage and current of the DC power supplied to the battery through the controller.
    • 当输入具有与电池的充电电压对应的电压的直流电力时,或者当输入具有高于电池的充电电压的电压的直流电力时,通过多电源电池充电器对电池进行完全充电 根据电池充电器输入的直流电力的电压,根据电池的充电电压来控制电池充电的控制器与DC / DC之间切换输入直流电力的供给目的地 转换器,其通过控制器控制提供给电池的直流电力的电压和电流。
    • 35. 再颁专利
    • Inference rule determining method and inference device
    • 推理规则确定方法和推理装置
    • USRE36823E
    • 2000-08-15
    • US542852
    • 1995-10-13
    • Hideyuki TakagiIsao Hayashi
    • Hideyuki TakagiIsao Hayashi
    • G05B13/02G06N5/04G06N7/04G05B13/00
    • G06N7/046G05B13/0285G06N5/048
    • An inference rule determining process according to the present invention sequentially determines, using a learning function of a neural network model, a membership function representing a degree which the conditions of the IF part of each inference rule is satisfied when input data is received to thereby obtain an optimal inference result without using experience rules. The inventive inference device uses an inference rule of the type "IF . . . THEN . . ." and includes a membership value determiner (1) which includes all of IF part and has a neural network; individual inference quantity determiners (21)-(2r) which correspond to the respective THEN parts of the inference rules and determine the corresponding inference quantities for the inference rules; and a final inference quantity determiner which determines these inference quantities synthetically to obtain the final results of the inference. If the individual inference quantity determiners (2) each has a neural network structure, the non-linearity of the neural network models is used to obtain the result of the inference with high inference accuracy even if in object to be inferred is non-linear.
    • 根据本发明的推理规则确定过程顺序地确定使用神经网络模型的学习功能,表示在接收到输入数据时满足每个推理规则的IF部分的条件的程度的隶属函数,从而获得 没有使用经验规则的最佳推理结果。 本发明的推理装置使用类型“IF ... THEN ...”的推理规则。 并且包括隶属值确定器(1),其包括所有的IF部分并具有神经网络; 单个推理量确定器(21) - (2r),其对应于推理规则的相应THEN部分并且确定推理规则的相应推理量; 以及最终推理量确定器,其合成地确定这些推理量以获得推断的最终结果。 如果个体推理量确定器(2)各具有神经网络结构,则即使在被推断的对象是非线性的,也使用神经网络模型的非线性度来获得具有高推理精度的推理结果。