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Update neuron.md #731

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4 changes: 2 additions & 2 deletions docs/tutorials/deep_learning/basic_concepts/neuron.md
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<center>图1 MCP 模型结构</center><br></br>

从 **图1** 可见，给定 n 个二值化（0或1）的输入数据 $x_i$ ($1\le i\le n$)与连接参数 $w_i$ ($1\le i\le n$)，MCP 神经元模型对输入数据线性加权求和，然后使用函数 $\varPhi \left( \right)$ 将加权累加结果映射为 0 或 1 ，以完成两类分类的任务：
$$
$
y=\varPhi \left( \sum_{i=1}^n{w_ix_i} \right)
$$
$

其中 $w_i$ 为预先设定的连接权重值（一般在 0 和 1 中取一个值或者 1 和 -1 中取一个值），用来表示其所对应输入数据对输出结果的影响（即权重）。$\varPhi \left( \right)$ 将输入端数据与连接权重所得线性加权累加结果与预先设定阈值 $\theta$ 进行比较，根据比较结果输出 1 或 0。

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