2002 · 44 citations · 2 references
ComputingComputational EngineeringComputer ScienceArtificial IntelligenceMachine LearningOptimizationFuzzy LogicNeural NetworksRule-based DesignRule-based RepresentationFuzzy ControlFuzzy Inference SystemsFuzzy Rule-based SystemsMembership FunctionsNeural Network ModelingRule Learning
A novel fuzzy logic design, called Neufuz, using neural net learning is proposed. Artificial neural net algorithms are used to generate fuzzy rules and membership functions. The combination of learned fuzzy rules, membership functions, and a fuzzy design technique based on new fuzzy inferencing and defuzzification methods significantly improves performance, accuracy, and reliability and reduces design time. Neufuz also minimizes system cost by optimizing the number of rules and membership functions. Simulation results are very encouraging.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Fuzzy neural network with fuzzy signals and weights
Yoichi Hayashi, James J. Buckley, E. Czogała · 2003 · 26 citations