Do I need to normalise input and target data when training an ANN using fitnet?

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Muhammad Usman Saeed
评论： Muhammad Usman Saeed 2025-6-9
I am training an artificial neural network using MATLAB's `fitnet` function with 5 input variables (e.g., compression ratio, HES, SOI, injection duration, RPM) and 10 outputs (e.g., BTE, NOx, BSFC, Torque, CO, etc.).
I understand that `fitnet` automatically normalises input data using `mapminmax`. However, I am not sure whether I also need to manually normalise the **target (output)** data before training.
Do I need to normalise the **outputs** manually, especially when they vary widely in scale (e.g., NOx in ppm, BTE in %, BSFC in kg/kWh)?
Also, what is the correct way to **denormalise predictions** after training?
Any clarification would be appreciated.

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回答（1 个）

Manish
Manish 2025-6-7
Hi,
The 'mapminmax' function scales inputs and targets to fall within the range [–1, 1].
The following code demonstrates how to use it:
[pn,ps] = mapminmax(p);
[tn,ts] = mapminmax(t);
net = train(net,pn,tn);
No manual effort is required for normalization.
If needed, you can reverse the normalization using the 'reverse' option with 'mapminmax'.
Refer to the documentation link for better understanding:
  • https://www.mathworks.com/help/deeplearning/ref/mapminmax.html
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Muhammad Usman Saeed
Muhammad Usman Saeed 2025-6-9
Thank you so much for your response!
Could you please let me know should I denormalise the predections or it is also denormalised automatially?

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