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The research of operation accuracy of a memristor-based artificial neural network with an input signal containing noise and pulse interference

机译:基于忆阻器的人工神经网络在输入信号中包含噪声和脉冲干扰的工作精度研究

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This article looks at the issues of calculating the operation accuracy of memristor-based hardware when there are some pulse interference (ε-polluted interference with additive white Gaussian noise) in a square signal. Through the example of operation of the memristor-based artificial neural network synapse, you can see that this type of interference in an input signal of memristor-based hardware causes additional error in the values of their output parameters. It was revealed that there is correlation dependence between the values of parameters of noise components in an input signal of a memristor-based artificial neural network (noise and interference variance, an occurrence probability of pulse interference) and the value of synaptic weight.
机译:本文着眼于在方波信号中存在一些脉冲干扰(ε污染和加性高斯白噪声)的情况下,计算基于忆阻器的硬件的工作精度的问题。通过基于忆阻器的人工神经网络突触的操作示例,您可以看到,基于忆阻器的硬件的输入信号中的这种类型的干扰会导致其输出参数的值出现其他误差。揭示了基于忆阻器的人工神经网络的输入信号中的噪声分量的参数值(噪声和干扰方差,脉冲干扰的发生概率)与突触权重值之间存在相关性。

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