Perceptron Learning Algorithm In Machine Learning

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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