神经网络谐波分析matlab代码

Sure! Here's an example of MATLAB code to perform harmonic analysis using a neural network:

% Generate input data
t = 0:0.001:2*pi;   % Time vector
f1 = sin(2*pi*50*t);  % Generate a sine wave with frequency 50Hz
f2 = sin(2*pi*120*t); % Generate a sine wave with frequency 120Hz
f = f1 + f2;         % Combine the two sine waves

% Prepare training data
inputs = f(1:end-1);    % Input sequence
targets = f(2:end);     % Target sequence

% Create and train the neural network
net = feedforwardnet(10);     % Create a feedforward neural network with 10 hidden neurons
net = train(net, inputs, targets);   % Train the neural network

% Predict the next value in the sequence
predicted = net(inputs);

% Plot the original and predicted sequences
figure;
hold on;
plot(t(1:end-1), targets, 'b');     % Plot the target sequence in blue
plot(t(1:end-1), predicted, 'r--'); % Plot the predicted sequence in red dashed line
xlabel('Time');
ylabel('Amplitude');
legend('Target', 'Predicted');
title('Harmonic Analysis');

In this code, we first generate two sine waves with frequencies of 50Hz and 120Hz, respectively. We then combine these two sine waves to create a composite signal. The feedforward neural network is then trained using this composite signal, with the input sequence being the composite signal except for the last value, and the target sequence being the composite signal starting from the second value.

After training the network, we predict the next value in the sequence using the trained network. Finally, we plot both the target sequence and the predicted sequence to visualize the results.

Please note that this is just a basic example, and you may need to modify it according to your specific requirements.