A MLP model with two input neurons, two hidden neurons and an output neuron.

most commonly used function for the computing nodes to process

on and information transmission is called the sigmoid function.

functions (݂ଵ, ݂ଶ and ݂ଷ) use this sigmoid function for processing,

nd bypassing information, where the input to the sigmoid function

ted signal from lower-layer neurons. Figure 3.22 shows the

function. The sigmoid function transforms the interval (−∞, ∞)

nterval (0, 1). For instance, the input for ݖଵ is shown below, where

are the lower-layer (input layer) neurons (or signals),

ݓଵଵൈݔଵ൅ݓଵଶൈݔଶ

(3.31)

output of ݖଵ is defined as a sigmoid function which is shown

1

1 ൅݁^{ି௪భభൈ௫భି௪భమൈ௫మ}

(3.32)

utput of ݖଶ and the output of y employ the similar function.

The sigmoid function. The x-coordinate is the input of a sigmoid function and

inate is the output of a sigmoid function.