how to smooth data in matlab in matlab

To smooth data in Matlab, you can use various filtering techniques such as moving average, median filtering, low-pass filtering, or Savitzky-Golay filtering. The choice of filter depends on the nature of your data and the specific requirements of your analysis.

Here's an example of how to apply a moving average filter to a noisy signal:

main.m
% Generate noisy signal
t = linspace(0, 2*pi, 100);
x = sin(t) + 0.1*randn(size(t));

% Apply a moving average filter
windowSize = 5;
b = (1/windowSize)*ones(1, windowSize);
a = 1;
y = filter(b, a, x);

% Plot the results
plot(t, x, 'b-', t, y, 'r-', 'LineWidth', 1.5);
legend('Noisy signal', 'Smoothed signal');
xlabel('Time');
ylabel('Amplitude');
title('Moving average filter');
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In this example, we first generate a noisy signal using the sin function and Gaussian noise. We then define a moving average filter with a window size of 5 by setting the coefficients b to 1/5 and a to 1. Finally, we apply the filter to the signal using the filter function, and plot both the original and smoothed signals using plot. The resulting plot shows that the moving average filter effectively removes the high-frequency noise from the signal, while preserving the underlying trend.