Data-driven fuzzy clustering based on maximum entropy principle and PSO
To identify the optimum fuzzy rule base is the major difficulty in designing fuzzy model. To design optimum fuzzy rule base, which is traditionally achieved by tedious trial and error process, from numerical data, a novel data-driven fuzzy clustering method based on maximum entropy principle (MEP) and particle swarm optimization (PSO) is proposed. In this algorithm, the memberships of output variables are inferred by maximum entropy principle, and the centers of fuzzy rule base are optimized by PSO. Comparing with the method that designing fuzzy rule base only by PSO or other evolutionary computation methods, the number of parameters to be optimized decreased greatly, and the computation cost declined. To check the effectiveness of the suggested approach, three examples for modeling are examined comparing with the method only using PSO. The performance of the identified fuzzy models is demonstrated.
fuzzy modeling maximum entropy principle (mep) particle swarm optimization (pso) nonlinear system