Train the neural network using a two-input XOR gate knowing the initial values: w1 = 0.9; w2 = 1,8; b = - 0.9; Requirements achieved: Analyze the steps to train a perceptron

Train the neural network using a two-input XOR gate knowing the initial values:

w1 = 0.9;

w2 = 1,8;

b = - 0.9;

Requirements achieved:

Analyze the steps to train a perceptron neural network.

Training programming using Matlab software.

Use nntool for survey and analysis

[Manikanta Aditya]

Here, I didn't get what you are trying to ask. Are you having any issue or asking us to train the entire thing?

Answer 1

[Shubham]

Hi @ga,

Training a perceptron neural network to learn the XOR function is a classic problem in neural network theory. However, it's important to note that a single-layer perceptron cannot solve the XOR problem due to its non-linearly separable nature. Instead, a multi-layer perceptron (MLP) with at least one hidden layer is required.

Nevertheless, if you want to proceed with the exercise using a single-layer perceptron for educational purposes, we can simulate the process using MATLAB. Here's how you can approach it:

Step-by-Step Guide to Train a Perceptron

1. Initialize Parameters:

• We start with the given initial weights and bias

w1 = 0.9;
w2 = 1.8;
b = -0.9;

2. Define the XOR Input and Output:

• The XOR function truth table is:

inputs = [0 0; 0 1; 1 0; 1 1]';
targets = [0 1 1 0];

3. Create and Train the Perceptron:

• Use MATLAB's Neural Network Toolbox to create and train the perceptron.

% Create a perceptron
net = perceptron;

% Set initial weights and bias
net.IW{1,1} = [w1 w2];
net.b{1} = b;

% Train the perceptron
net = train(net, inputs, targets);

4. Analyze the Network:

• After training, you can analyze the network's performance using MATLAB's tools.

% Simulate the network
outputs = net(inputs);

% View the network
view(net);

% Plot the performance
figure;
plotperform(net);

5. Use nntool for Survey and Analysis:

• nntool is a graphical user interface in MATLAB that allows you to create, train, and simulate neural networks.

nntool;

• In nntool, you can import your network and data, perform training, and visualize results.

Note on XOR and Perceptrons

As mentioned, a single-layer perceptron cannot solve the XOR problem because it cannot represent non-linearly separable functions. To solve the XOR problem, consider using a multi-layer perceptron (MLP) with a hidden layer, which can be implemented using the feedforwardnet function in MATLAB.

% Create a feedforward neural network with one hidden layer
net = feedforwardnet(2);

% Train the network
net = train(net, inputs, targets);

% Simulate the network
outputs = net(inputs);

% View the network
view(net);

% Plot the performance
figure;
plotperform(net);

This approach will enable you to successfully train a neural network to learn the XOR function.