Proceedings of IEEE 5th International Fuzzy Systems · 2002 · 378 citations · 16 references
ComputingAutomotive EngineeringComputer ScienceArtificial IntelligenceMachine LearningPredictive AnalyticsEnergy Demand ForecastingExpert SystemsFuzzy LogicNeural NetworksPattern RecognitionSystem IdentificationAdaptive InferenceFuzzy Inference SystemsFuzzy Rule-based SystemsInput OptimizationNonlinear RegressionNonlinear Regression ModelsNonlinear System IdentificationNonlinear System Modeling
We present a quick and straightfoward way of input selection for neuro-fuzzy modeling using adaptive neuro-fuzzy inference systems (ANFIS). The method is tested on two real-world problems: the nonlinear regression problem of automobile MPG (miles per gallon) prediction, and the nonlinear system identification using the Box and Jenkins gas furnace data.
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