We have been given a neural network with the input layer and output layer. The input layer has three neurons, and the output layer has one neuron. In this case, 0.8, 0.6, and 0.4 are the inputs, and 0.35 is the bias. The weights associated with the three neurons are 0.1, 0.3, and -0.2. There are two types of sigmoid functions: the first one is the binary sigmoid activation function. The equation of the binary sigmoid activation function is \( y = F(y) \), which is equivalent to \( \frac{1}{1 + e^{-y}} \), where \( y_n \) is the net input to that particular neuron. For example, if you want to find the output of \( y \), you need to find out the net input to this particular neuron. With the help of that, you can find out what the total output of this particular neuron is. In this case, similarly, we have one more sigmoid activation function, which is the bipolar sigmoid activation function. The equation looks something like this: \( y = F(y) \), which is equivalent to \( \frac{2}{1 + e^{-y}} - 1 \). In this case, again, we need to know what the net input to this particular \( y \) is so that we can calculate the output here. Now, to calculate this particular \( y_n \), we need to find the sum of the product of input and weight. The equation looks something like this: Where \( n \) is the number of input neurons, which is 3 in this case. If I expand this particular equation, it looks something like this: Now, we need to put all these particular values. \( B \) value is 0.35, \( x_1 \) is 0.8, \( w_1 \) is 0.1, \( x_2 \) is 0.6, \( w_2 \) is 0.3, \( x_3 \) is 0.4, and \( w_3 \) is -0.2. Now, if I solve this particular equation: \[ y_n = 0.35 + (0.8 \times 0.1) + (0.6 \times 0.3) + (0.4 \times -0.2) \] I will get 0.53 as the net input to this particular \( y \). once you get this particular net input you can put this value in the sigmoid functions so that you can get the output here so first we will see the output of a binary sigmoid activation function I will put that particular 0.53 in this equation I will get 0.625 as the output here so if I use binary Sigma and activation function the output of this neuron Y is 0.625 similarly if I put that net input that is 0.53 in bipolar sigmoid activation function I will get 0.259 that is the output of this particular neuron why in this case so this is a very simple example uh with which you can easily understand the sigma and activation function or how to use sigmet activation function to find the output of a particular neuron in this case