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% Range of input values [min max] for both dimensions input_range = [0 1; 0 1]; % Create the perceptron network net = newp(input_range, 1); Use code with caution. Step 3: Train the Network

: Steps for defining network architecture and initializing weights. When searching online for terms like "introduction to

A single artificial neuron (perceptron) calculates the weighted sum of its inputs, adds a bias, and passes the result through an activation function:

: Monitoring training progress and evaluating accuracy through tools like confusion matrices and mean squared error plots. Conclusion Using MATLAB’s plotting tools to show how

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% Test the network outputs = sim(net, inputs); adds a bias

This is for personal study — neural network fundamentals, backpropagation, and MATLAB implementation.

Note: It is highly recommended to seek the official published version for the highest accuracy. Conclusion

Using MATLAB’s plotting tools to show how the decision boundary changes as the network learns.