Algorithm 1. FNN towards the desired model.

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).

Table 1: The results of the simulation for two given functions of hypothetical examples and different techniques. (The total time of the experiments is in seconds).

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

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