Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
Predicting a chaotic time series using a fuzzy neural network
Information Sciences: an International Journal
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
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In this study, we introduce a new category of ANFIS-based fuzzy inference systems with the aid of information granulation to carry out the model identification of complex and nonlinear systems. To identify the structure of fuzzy rules we use genetic algorithms (GAs). Granulation of information with the aid of Hard C-Means (HCM) clustering algorithm help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms and the least square method (LSM). The proposed model is contrasted with the performance of the conventional fuzzy models in the literature.