1. The first layer provides the fuzzification of each variable. This is a parametrical layer with the parameters, subjected to the adaptation in the process of training. |
2. The second layer provides the aggregation of separate variables, defining the resulting value of the coefficient of belonging (the non-parametric layer). |
3. The third layer is the function generator which calculates its values. In this case, the weighting coefficient determines the model inference function which has to be adapted. |
4. The fourth layer consists of two nerve units-summers in which one of them calculates the weighted signal sum, and the second is the integral weight (the non-parametric layer). |
5. The fifth layer is the normalizing layer in which the output signal of network is aggregated according to Eq. (11). |
Functions | Full-Bust | Gradient Descent |
Takagi-Sugeno Fuzzy Model (Proposed) |
|
---|---|---|---|---|
with Neural Network |
with Genetic Algorithm |
|||
F | 380.7 | 58.3 | 21.5 | 11.2 |
G | 1520.4 | 142.1 | 70.4 | 54.1 |