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WEIGHTED MAJORITY DECISION PROCESSING SYSTEM BY NEURAL NETWORK

机译:神经网络的加权加权决策处理系统

摘要

PURPOSE:To output an estimation value with less errors by transmitting the output of a neural network and the output of a weight value addition means to a division means, obtaining the quotient and outputting the estimation value. CONSTITUTION:An input signal is mask-controlled lest it adds '0' of a weight value by using the neural network 1 which is learnt and adjusted, and the sum SIGMAWi of the weight values except for the weight value is outputted from the weight addition part 5. Then, XSIGMAWj and SIGMAWj outputted from the neural network 1 are divided in the division means 6 and the estimation value X is obtained. Thus, an operation can be executed in a majority decision graph surface without a discontinuous point. Thus, the value which the neural network can easily learn and which has less errors can be obtained.
机译:目的:通过将神经网络的输出和权重值相加装置的输出传送到除法装置,获得商并输出估计值,以输出误差较小的估计值。组成:一个输入信号是受掩码控制的,以免它使用学习和调整的神经网络1将权重值的“ 0”相加,并且权重值除权重值外的总和SIGMAWi从权重相加中输出然后,从神经网络1输出的XSIGMAWj和SIGMAWj在分割装置6中被分割,并且获得估计值X。因此,可以在没有不连续点的多数决策图表面中执行操作。因此,可以获得神经网络可以容易地学习并且具有较少误差的值。

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