Question: When we were training the anfis system, does over-fitting occur when we have a large epoch number instead of when we have large membership function number? Over-fitting =
When we were training the anfis system, does over-fitting occur when we have a large epoch number instead of when we have large membership function number?
the result when anfis automatically reduces training error in each epoch, making the system super-fits the training data, but may not fit other data in general
Our best fis system is the one called “ampFisUnfiltered3.fis” which uses the following criteria:
Using the following code, we can estimate temperatures if we know avgAmp(average amplitude, our measured feature)
Which is considerably big, about 15% of true value.
Could it be that our fis, “ampFisUnfiltered3.fis” is not a good model to predict this relationship between avgAmp and temperature?
right Skew - the mass of the distribution is concentrated on the left of the figure. It has relatively few high values
Also referred to as positive skew
Another thing to be noticed, exactly 105 out of the 130 big-error data points are measured under stressed condition (elevated temperature)
Average amplitude of chicken vocalization as a feature of stress level works well under low-stress environment, but produces wider ranges of fluctuation under high stressed ones.
Type of membership function=‘gaussmf’
While holding other variables constant
Let’s see the behavior of this new fis system:
The average error and standard deviation of error vary within the two fis systems