Fuzzy rule base optimisation: a pruning and merging approach

The paper deals with the simplification of the rule base of the Takagi-Sugeno model. The proposed technique assumes an initial lattice partition of the premise space. The complexity of the rule base is optimised through two sequential operations: elimination of the less important rules followed by the merging of neighbouring rules that can describe the same behaviour of the system. The identified structure is further refined by adjusting the parameters of the membership functions. The procedure is illustrated on a simulation example