Abstract
A methodology and experimental comparison of neuro-fuzzy structures, namely linguistic and zero and first-order Takagi-Sugeno, are developed. The implementation of the model is conducted through the training of a neuro-fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the first phase, the structure of the model is obtained by subtractive clustering, which allows the extraction of a set of relevant rules based on a set of representative input-output data samples. Membership functions with two-sided Gaussian functions are proposed and discussed. In the second phase, the model parameters are tuned via the training of a neural network. Furthermore, different fuzzy operators are compared, as well as regular and two-sided Gaussian functions.