threshold and bias in neural network

Question

I'm a newbie..I don't understand why we use threshold and bias in MLP (Multi-Layer Perceptron)??What is the role of threshold, bias..And I don't know the formula of output (the result after we use the activation function, such as sigmoid) follow bias and threshold..In the same document, I see:

output_value=activation_function(summing_function+threshold) (follow Jeff Heaton)
output_value=activation_function(summing_function-threshold)(follow my teacher)
output_value=activation_function(summing_function+bias) (no problem!)

Which is the correct??Please give me a response!

And, the bias and the threshold can exists same time in MLP??

Answer 1

MLP 中的偏差和阈值是相同的概念，只是同一事物的两个不同名称。符号并不重要，因为偏差可以是正的，也可以是负的（但更常见的是使用+bias）。

用最简单的术语来说 - 如果没有偏差，那么对于仅 的输入0，您会得到summing_function=0， 结果也是output_value=0（因为大多数激活函数都会跨越原点）。因此，您的网络无法学习此类信号的任何其他行为，因为整个模型中只有权重发生变化。

从更数学的角度来看——这负责改变激活函数并赋予神经网络通用逼近器功能。