Complexity reduction of rule based models: a survey

Gives a survey of fuzzy rule base reduction methods. The complexity reduction methods originate from two aspects depending on different design methodologies. The first model design type comes from the original idea of Zadeh, it proposes models which are built based on expert knowledge, hence the rule base is set up manually. These models feature linguistic, and hence semantically interpretable fuzzy terms, and rules with fuzzy sets as consequents. Secondly, data-driven fuzzy model design has become more popular. For fitting the model to the approximated function, these models, usually having rules with consequents which are linear function of the inputs, use tremendously large number of rules, and do not take into account the complexity and interpretability of the model. This feature also emerged in the issue of rule base reduction for such systems. The paper aims at summarizing the efforts in the complexity reduction field briefly