1. Click on the SELECT GATE tab.
2. Choose a gate from the dropdown menu and press the SUBMIT button.
3. Navigate to the COMPUTE tab in the header and click on it.
4. To initialize weight and bias values, click on the INITIALIZE button (random value).
5. Proceed to select values for the input nodes, specifically X₁ and X₂. Confirm your selections by clicking the SUBMIT button.
6. Identify the Y_in cell corresponding to the current row and click on it. Then, input the appropriate (Σ + Bias) value.
7. To apply the step function, click the ACTIVATE button.
8. Now, confirm the accuracy of the result by clicking the CHECK button to compare the expected value (T) with the actual value (Y).
9. If T does not match Y, follow these steps; otherwise, jump to step 10:
1. Click the UPDATE button to adjust weights and bias value.
2. Initialize the learning rate value (skip this step if already done).
3. Enter the required values in the input boxes for W₁(new) and click the SUBMIT button.
   Note: Observe the w₁(old), w₂(old), and b(old) values from the network, and the T, Y, and x values from the table.
4. Repeat the above step(c) for W₂(new) and b(new).
10. Repeat steps 5 through 9 for the other (total = 2²) combinations of X₁ and X₂.
11. Proceed by clicking the NEXT button to execute epochs until the predicted output is obtained for each input dataset.
12. Lastly, proceed to the ANALYZE tab and select it. Then, plot the graph by clicking the PLOT button.

Formulae:

1. Summation :

   Σ = w₁ . x₁ + w₂ . x₂ + ....+ wₙ . xₙ

2. Step function :

   Y_in = Σ + Bias

   f(y) = 1, if Y_in >= 0

   f(y) = -1, if Y_in < 0

3. Weight adjustment :

   Δw = η * d * x

Note : Read the alerts properly.