A method of generating control rule model and its application
Fuzzy Sets and Systems
Automatically determine the membership function based on the maximum entropy principle
Information Sciences: an International Journal
Identification model based on the maximum information entropy principle
Journal of Mathematical Psychology
On the maximum entropy parameterized interval approximation of fuzzy numbers
Fuzzy Sets and Systems
Fuzzy function approximation with ellipsoidal rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid clustering and gradient descent approach for fuzzymodeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A systematic approach to a self-generating fuzzy rule-table forfunction approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Online data-driven fuzzy clustering with applications to real-time robotic tracking
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Fuzzy control of pH using genetic algorithms
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Fuzzy-based dialectical non-supervised image classification and clustering
International Journal of Hybrid Intelligent Systems
Improving fuzzy knowledge integration with particle swarmoptimization
Expert Systems with Applications: An International Journal
Short Communication: Image segmentation using PSO and PCM with Mahalanobis distance
Expert Systems with Applications: An International Journal
Linguistic fuzzy model identification based on PSO with different length of particles
Applied Soft Computing
Hi-index | 12.06 |
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.