  Fuzzy systems have achieved great success in numerous applications. However,
there are still many challenges in designing an optimal fuzzy system, e.g., how
to efficiently optimize its parameters, how to balance the trade-off between
cooperations and competitions among the rules, how to overcome the curse of
dimensionality, how to increase its generalization ability, etc. Literature has
shown that by making appropriate connections between fuzzy systems and other
machine learning approaches, good practices from other domains may be used to
improve the fuzzy systems, and vice versa. This paper gives an overview on the
functional equivalence between Takagi-Sugeno-Kang fuzzy systems and four
classic machine learning approaches -- neural networks, mixture of experts,
classification and regression trees, and stacking ensemble regression -- for
regression problems. We also point out some promising new research directions,
inspired by the functional equivalence, that could lead to solutions to the
aforementioned problems. To our knowledge, this is so far the most
comprehensive overview on the connections between fuzzy systems and other
popular machine learning approaches, and hopefully will stimulate more
hybridization between different machine learning algorithms.
