Train a Signal in Machine Learning Toolbox

Hi, I've been using the machine learning toolbox for regression applications and I was wondering how you can use a regression model on matlab to train a certain signal that is over a period of time. For example if I wanted to train a model to multiply a sinusoidal signal's amplitude by 5 such that when i input any sinusoidal signal the amplitude multiplies by 5, how do i go about that?

Answer 1

[Yash]

Hello Hamza,

I understand you want to train a regression model in MATLAB to multiply a sinusoidal signal's amplitude by 5. To achieve this you can follow the below steps:

1. Obtain the dataset on which you want to train your model.training data. For your problem statement you can generate a range of sinusoidal signals with varying amplitudes and calculate the desired output by multiplying the amplitude by 5.
2. Organise your training data into input and output matrices. The input matrix should consist of the features of the sinusoidal signal, such as time or frequency domain values. The output vector should contain the desired multiplied amplitudes.
3. Choose an appropriate regression model that best fits your requirements, such as linear regression, support vector regression, or neural networks. For this particular task, a simple linear regression model should be sufficient.
4. Train the regression model in MATLAB using the provided training data. Utilize built-in functions like 'fitlm' for linear regression or explore other regression functions based on the chosen model. Provide the input and output data to the training function, allowing MATLAB to estimate the model parameters.
5. Evaluate the trained regression model using separate validation data, covering various sinusoidal signals. Compare predicted outputs with actual multiplied amplitudes.
6. After validating the model, use it to predict the multiplied amplitude for new sinusoidal signals (test data) by providing input values to the model.

You can refer to the following example for more information: https://in.mathworks.com/help/stats/train-linear-regression-model.html

Ensure to adapt the complexity of the model and the size of the training dataset according to the specific requirements and characteristics of your signals. It is crucial to find the right balance to avoid underfitting or overfitting the data.

Hope this helps!

[Hamza Farah]

Hi Yash, thank you very much for this reply! I understand the inputting of the data into the training model but my main issue was actually the first two steps mentioned. How exactly do I generate a range of sinusoidal signals on matlab and then organise them into input and output matrices? That was my main issue, how to organise the data essentially, as I am not sure how to implement time series data. Thanks again for the help