发明授权
US5333239A Learning process system for use with a neural network structure data
processing apparatus
失效
用于神经网络结构数据处理装置的学习过程系统
- 专利标题: Learning process system for use with a neural network structure data processing apparatus
- 专利标题(中): 用于神经网络结构数据处理装置的学习过程系统
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申请号: US3856申请日: 1993-01-11
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公开(公告)号: US5333239A公开(公告)日: 1994-07-26
- 发明人: Nobuo Watanabe , Takashi Kimoto , Akira Kawamura , Ryusuke Masuoka , Kazuo Asakawa
- 申请人: Nobuo Watanabe , Takashi Kimoto , Akira Kawamura , Ryusuke Masuoka , Kazuo Asakawa
- 申请人地址: JPX Kawasaki
- 专利权人: Fujitsu Limited
- 当前专利权人: Fujitsu Limited
- 当前专利权人地址: JPX Kawasaki
- 优先权: JPX63-227825 19880912
- 主分类号: G06N3/04
- IPC分类号: G06N3/04 ; G06N3/08 ; G06F15/18 ; G06G7/60
摘要:
A learning process system is provided for a neural network. The neural network is a layered network comprising an input layer, an intermediate layer and an output layer formed of basic units. In the basic units, a plurality of inputs is multiplied by a weight signal and the products are accumulated, thereby supplying the sum of products. An output signal is obtained using a threshold value function in response to the sum of products. An error signal is generated by an error circuit in response to a difference between the output signal obtained from the output layer and a teacher signal. A weight updating signal is determined in a weight learning circuit by obtaining a weight value in which the sum of the error values falls within an allowable range. Thus, the learning is performed in the layered neural network through use of a back propagation method. Through such learning in the layered neural network, an updating quantity to be obtained in the present weight updating cycle is determined in response to a once delayed weight updating quantity signal in a previous weight updating cycle and a twice delayed weight updating quantity obtained at a twice-previous weight updating cycle prior to the previous weight updating cycle.
公开/授权文献
- US5968367A High performance liquid chromatography method and apparatus 公开/授权日:1999-10-19
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