rbf matlab

RBF stands for Radial Basis Function. In MATLAB, you can use the built-in function "rbf" to create radial basis function networks. The "rbf" function is part of the Neural Network Toolbox.

Here's a simple example of using the "rbf" function in MATLAB:

% Create training data
x = -5:0.1:5;
y = sin(x);

% Create an RBF network
net = newrb(x, y);

% Generate predictions
x_test = -5:0.01:5;
y_pred = sim(net, x_test);

% Plot the results
plot(x, y, 'b');
hold on;
plot(x_test, y_pred, 'r');
legend('Training Data', 'RBF Predictions');

In this example, we first create some training data with input values `x` and corresponding output values `y`. Then, we create an RBF network using the `newrb` function, which automatically determines the number of radial basis functions to use.

We then generate predictions for a range of test input values `x_test` using the `sim` function. Finally, we plot the training data and the RBF predictions to visualize the results.

Note that you may need to have the Neural Network Toolbox installed in MATLAB to use the "rbf" function.