Predicting the output of a machine based on the data in hand

Hello everyone,

I'm running some experiments in a lab on a machine and i'm trying to simulate the output based on these experiments and a governing equation that relates the materials used in the machine to the relative humidity in the atmosphere. I'm running the machine at different levels of relative humidity and recording the outputs for each individual material. I repeat the tests and get the output data with 15 trials for three different kinds of materials( 5 for each material, i.e, output for the individual material at five levels of relative humidity). I want to use this data along with the above mentioned governing equation to predict the output for a fourth material at differrent levels of humidity. Is this possible with the deep learning toolbox? Can i have some tips on where to start with this idea? I can look up the documentation and proceed but since i'm totally new to neural networks, it'd be helpful if someone could share some valuable information/tips on where to begin or the add-ons that i require for this.

Cheers!

Answer 1

[Shravan Kumar Vankaramoni]

Hi,

I understand that it is a regression problem. It is possible to implement your scenario using deep learning toolbox. The following docs should help you to start

Sequence to Sequence regression using deep learning

Shallow neural network

Create a deep learning experiment for regression

Deep learning tips and tricks

If you have a specific use case, you can go with NN otherwise you can go with ML regression models using Statistics and Machine Learning toolbox .

https://www.mathworks.com/help/stats/train-regression-models-in-regression-learner-app.html