Identification of ANFIS-Based Fuzzy Systems with the Aid of Genetic Optimization and Information Granulation

Abstract

AbstractIn 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.KeywordsFuzzy RuleFuzzy ModelFuzzy Inference SystemRecurrent Neural NetworkInformation GranulationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.