From knowledge-based to data-driven modeling of fuzzy rule-based
  systems: A critical reflection
  This paper briefly elaborates on a development in (applied) fuzzy logic that
has taken place in the last couple of decades, namely, the complementation or
even replacement of the traditional knowledge-based approach to fuzzy
rule-based systems design by a data-driven one. It is argued that the classical
rule-based modeling paradigm is actually more amenable to the knowledge-based
approach, for which it has originally been conceived, while being less apt to
data-driven model design. An important reason that prevents fuzzy (rule-based)
systems from being leveraged in large-scale applications is the flat structure
of rule bases, along with the local nature of fuzzy rules and their limited
ability to express complex dependencies between variables. This motivates
alternative approaches to fuzzy systems modeling, in which functional
dependencies can be represented more flexibly and more compactly in terms of
hierarchical structures.
