AbstractTo find an optimal balance between accuracy and model transparency in fuzzy systems modeling, a novel approach based on the non-dominated sorting fireworks optimization algorithm (NSFOA) is proposed in this paper. The FOA is a new swarm intelligence algorithm mimicking the explosion of fireworks, which could be used to tune the parameters and optimize the structure of fuzzy systems with high effectiveness and correctness. At each iteration of the NSFOA algorithm, all individuals are sorted based on fast non-dominated sorting and crowding-distance evaluation, and the multiobjective fitness functions consider the aspects of both interpretability and accuracy contemporaneously. Subsequently, the individuals with better transparency and accuracy are selected for the next generation. The proposed NSFOA algorithm is applied to the second-order non-linear system and the Iris benchmark problem, and the experimental results demonstrate that our method leads to comprehensible fuzzy models with remarkable accuracy.
Pattern trees for regression and fuzzy systems modeling