I have a question regarding about improving the performance of an ANFIS (adaptive neuro Fuzzy inference system) model. In MATLAB, I have been training a model with 5 inputs, with 816 data point for each input, and 1 output, with 816 data points. The best model has the following properties:
- 2 memberships function for each input, pi-shaped.
- The output has constant membership function for each of the 64 rules. The training parameters are the following:
- Epoch: 500
- Initial step size: 0.1
- Step size decrease rate: 0.9
- Step size increase rate: 1.1
- Optimization method: Backpropagation with gradient descent.
- Clustering method: grid partition This is how the model output behaves:
ANFIS Model output (red) with desired output (blue)
From 0 to 568 Dias, is how the model behaves (red line) with the training dataset (blue), while from 569 to 815 Dias is the validation set. The model, for this problem, has to be ANFIS. Any kind of help is aprecciated.
I tried to change the memberships function, the number of them for each input, the membership function for the output, the number of epoch, the initial step size, decrease and increase rate, but it never improved more than 0.18 RMSE training and 0.2 RMSE validation.