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

On the Functional Equivalence of TSK Fuzzy Systems to Neural Networks, Mixture of Experts, CART, and Stacking Ensemble Regression