IEICE Transactions on Information and Systems · 2005 · 12 citations · 0 references
ComputingComputational EngineeringComputer ScienceArtificial IntelligenceData MiningMachine LearningNeural ModelingNeural NetworksAdaptive NetworksClusteringFuzzy Inference SystemsFuzzy Rule-based SystemsFuzzy SetsGranulationIf-then RulesRadial Basis Function Networks
In this paper, we present the method for identifying an Adaptive Neuro-Fuzzy Networks (ANFN) with Takagi-Sugeno-Kang (TSK) fuzzy type based on fuzzy granulation. We also develop a systematic approach to generating fuzzy if-then rules from a given input-output data. The proposed ANFN is designed by the use of fuzzy granulation realized via context-based fuzzy clustering. This clustering technique builds information granules in the form of fuzzy sets and develops clusters by preserving the homogeneity of the clustered patterns associated with the input and output space. The experimental results reveal that the proposed model yields a better performance in comparison with Linguistic Models (LM) and Radial Basis Function Networks (RBFN) based on context-based fuzzy clustering introduced in the previous literature for Box-Jenkins gas furnace data and automobile MPG prediction.