Constrained optimization of FIS: interpretability and accuracy
Pierre Yves Glorennec · 2004
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.