Based on what you have seen in the previous lectures regarding Perceptron and Perceptron learning rules, you are going to focus on designing a Perceptron Neural Network. You must train your associated Neural Network using Perceptron learning rule to determine its parameters so that it can be used as an “AND” logical gate. The truth table for an “AND” logical gate is shown below:
Task
Step 1, Initialisation
In this step, from above truth table you are first going to determine:
your training set (i.e., ideal data input to the network and expected output from the network which consists of Q input - output pairs { Pk , t k } where k = 1 , ... , Q).
Q (i.e., number of input – output pair in your training set using above truth table.
and design a neural network architecture and draw it manually
your neural network unknown weights and biases and randomly initialise them.
You must also show that your training set are linearly separable in input space by drawing a line that separated them. This is required as proves a solution exist.
Step 2, training
The Perceptron learning rule, as given in the lecture, update the weight vector and biases, using training error ek and following formula
which means in each epoch new weights and biases are updated from previous old weights
and biases according to
The training error ek is determined by formula
i.e. the difference between the target training output tk and actual output of network ak.
In this step you are going to implement Perceptron learning rule manually. It is important to
do it once with hand before starting programming.
You are required by your client to:
1. Develop an algorithm based on above learning rule to train your neural network.
2. You should use flowchart and explain your algorithm
3. Justify your steps
4. implement your algorithm for above task and for 3 epochs after initialization calculate
Step 2, test
The final step is to test your network with unseen data. This step is not required and won’t
be marked.
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