公开(公告)号：US5355434A

公开(公告)日：1994-10-11

申请号：US931324

申请日：1992-08-18

申请人： Takao Yoneda ,  Tomonari Kato ,  Kazuya Hattori ,  Masashi Yamanaka ,  Shiho Hattori

发明人： Takao Yoneda ,  Tomonari Kato ,  Kazuya Hattori ,  Masashi Yamanaka ,  Shiho Hattori

IPC分类号： G06N3/08 ,  G06N3/10 ,  G06F15/18

CPC分类号： G06N3/10 ,  G06N3/08

摘要： An apparatus for carrying out learning operation of a neural network which has an input layer, an intermediate layer and an output layer. Plural nodes of the input layer are related to plural nodes of the intermediate layer with plural connection weights, while the plural nodes of the intermediate layer are also related to plural nodes of the output layer with plural connection weights. Although input data composed of plural components and teaching data composed of plural components are used in learning operation, some of the components are ineffective for the purpose of learning operation. During error calculation between output data from the neural network and the teaching data, the apparatus judges whether each of the components of the teaching data is effective or ineffective, and output errors corresponding to the ineffective components are regarded as zero. The connection weights are thereafter corrected based upon the calculated errors. The apparatus further comprises means for adding new input data and teaching data into a data base, and means for calculating a degree of heterogeneousness of the new teaching data. The new input data and teaching data are added to the data base only when the degree of heterogeneousness is smaller than a predetermined value.

摘要翻译： 一种用于执行具有输入层，中间层和输出层的神经网络的学习操作的装置。 输入层的多个节点与具有多个连接权重的中间层的多个节点相关，而中间层的多个节点也与具有多个连接权重的输出层的多个节点相关。 虽然在学习操作中使用由多个分量组成的输入数据和由多个分量组成的教学数据，但是为了学习操作的目的，一些组件是无效的。 在神经网络的输出数据与教学数据的误差计算期间，设备判断教学数据的各个成分是否有效或无效，与无效分量相对应的输出误差为零。 然后根据计算的误差校正连接权重。 该装置还包括用于将新的输入数据和教学数据添加到数据库中的装置，以及用于计算新教学数据的异质程度的装置。 仅当异质度小于预定值时，才将新的输入数据和教学数据添加到数据库。

公开(公告)号：US5473532A

公开(公告)日：1995-12-05

申请号：US731472

申请日：1991-07-17

申请人： Kunihiko Unno ,  Yukinori Kakazu ,  Takao Yoneda ,  Moriaki Sakakura ,  Masashi Yamanaka ,  Shiho Hattori

发明人： Kunihiko Unno ,  Yukinori Kakazu ,  Takao Yoneda ,  Moriaki Sakakura ,  Masashi Yamanaka ,  Shiho Hattori

IPC分类号： G05B19/4093 ,  G05B19/416 ,  G06F17/00

CPC分类号： G05B19/4163 ,  G05B19/40937 ,  G05B2219/33027 ,  G05B2219/34065 ,  G05B2219/36284 ,  G05B2219/49061 ,  G05B2219/49088 ,  Y02P90/265 ,  Y10S706/904

摘要： An intelligent machining system employs a neural network for calculating machining conditions on the basis of attribute data or a workpiece and a grinding machine. The system comprises a reference machining condition calculating unit, a neural network which receives the attribute data and provides corrections, and a correcting unit for correcting the reference machining conditions by using the corrections. Corrections which cannot be determined by means of empirical expressions or theoretical expressions are determined appropriately by the neural network previously learned. The system determines corrections for the machining conditions on the basis of machining errors decided by a neural network. The system also detects time-series machining phenomena by sensors, processes the output detection signals of the sensors by a neural network to obtain machining circumstance data. The feed rate of the tool is controlled by a fuzzy inference on the basis of the machining circumstance data.

摘要翻译： 智能加工系统采用神经网络根据属性数据或工件和研磨机计算加工条件。 该系统包括参考加工条件计算单元，接收属性数据并提供校正的神经网络，以及通过使用校正来校正参考加工条件的校正单元。 通过经验表达式或理论表达式无法确定的修正由以前学习的神经网络适当确定。 系统基于由神经网络决定的加工误差来确定加工条件的校正。 该系统还通过传感器检测时间序列加工现象，通过神经网络处理传感器的输出检测信号，获得加工环境数据。 基于加工环境数据，通过模糊推理来控制刀具的进给速度。