A simply identified Sugeno-type fuzzy model via double clustering
Information Sciences—Informatics and Computer Science: An International Journal - Special issue on modeling with soft-computing
About the use of fuzzy clustering techniques for fuzzy model identification
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Numerical and applicational aspects of fuzzy relational equations
Fuzzy Sets and Systems
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Fuzzy polynomial neural networks: hybrid architectures of fuzzy modeling
IEEE Transactions on Fuzzy Systems
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We introduce information granulation-based fuzzy systems to carry out the model identification of complex and nonlinear systems. The proposed fuzzy model implements system structure and parameter identification with the aid of genetic algorithms (GAs) and information granulation (IG). The design methodology emerges as a hybrid structural optimization and parametric optimization. IG realized with Hard C-Means (HCM) clustering help determine the initial parameters of fuzzy. And the initial parameters are tuned effectively with the aid of the GAs and the least square method (LSM). And we use GAs to identify the structure of fuzzy rules.