Question
hi Guys! i have no experience with the neuronal toolbox. But i hope that i can solve these problem: My problem sounds not so difficult: i have different testing resaults. - 1 channel: enginge speed - 2 channel: preassure - 3 channel: temperature trend of the cooling medium my unknown signal is an other temperature trend. First of all i installed a temperature-senor on the place to get the unknown signal to study the behavior of heating. I tried 7 testing with different load cases. now i have 7 different behaviors of the temperatur trends dependent on the 3 channels. by the way, in the future i have to deinstall the sensor..... is it possible to solve this problem with the neural network toolbox? i want to create a kind of forecast of the temperature-trend with the help of the testing resaults.
Expert Answer
Neeta Dsouza
PhD Expert
Answered Aug 11, 2026
The construction of the net is basic. See the documentation, examples and demos.
The basic problem is that with N = 7 you probably do not have enough training data for an accurate I-H-O = 3-H-1
network. If you use all the defaults of FITNET or NEWFF, H = 10, Ntrn ~ 0.7*N = 5, Nval = 1 and Ntst = 1.
The number of unknown weights is Nw = (I+1)*H+(H+1)*O = 40+11 = 51
However, the number of training equations is only Neq = Ntrn*O = 5 << Nw
Possible solutions which can be combined:
1. Obtain more data.
2. Decrease H
3. Regularized training with TRAINBR
4. Simulate new data by adding noise to the current inputs (but not to outputs)
5. Average the outputs of multiple nets selected from
a. Many different weight initializations and data divisions.
6. I suggest you begin with
a. Standardize all variables to zero-mean/unit-variance
b. Calculate the 4-variable correlation coefficient matrix
c. Plot the 3 outputs vs the input
d. Ponder the results of c and d
e. Vary H with Ntrials(=20) weight-initializations for each
candidate value of H.
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