% Create a neural network architecture net = newff(x, y, 2, 10, 1);
% Train the neural network net = train(net, x, y); % Create a neural network architecture net =
% Test the neural network y_pred = sim(net, x); They consist of interconnected nodes or "neurons" that
% Evaluate the performance of the neural network mse = mean((y - y_pred).^2); fprintf('Mean Squared Error: %.2f\n', mse); This guide provides a comprehensive introduction to neural networks using MATLAB 6.0. By following the steps outlined in this guide, you can create and train your own neural networks using MATLAB 6.0. fprintf('Mean Squared Error: %.2f\n'
Neural networks are computational models inspired by the structure and function of the human brain. They consist of interconnected nodes or "neurons" that process and transmit information. Neural networks can learn from data and improve their performance over time, making them useful for tasks such as classification, regression, and feature learning.
% Create a sample dataset x = [1 2 3 4 5]; y = [2 3 5 7 11];