Constrained optimization of FIS: interpretability and accuracy

In fuzzy learning, interpretability and accuracy are often antagonistic. In many cases, this dilemma is usually overcome by the changeover from fuzzy inference systems to radial basis neural networks: the system performs well but the interpretability of fuzzy rules is lost. It is not a fatality: constrained optimization methods can both preserve interpretability and increase the accuracy of the fuzzy model.