Compressing and improving fuzzy rules using genetic algorithm and its application to fault detection

• 主要な著者: Yap K.S., Wong S.Y., Tiong S.K.
• その他の著者: 24448864400
• フォーマット: Conference Paper
• 出版事項: 2023
• 主題: Fault Detection; Fuzzy Inference System; Genetic Algorithm; Rule Extraction; Factory automation; Fault detection; Genetic algorithms; Neural networks; Adaptive resonance theory; Bench-mark problems; Classification performance; Fuzzy inference systems; Generalized Regression Neural Network(GRNN); Hybrid neural networks; Prediction process; Rule extraction; Fuzzy rules

The fuzzy rule sets, which have been derived from the hybrid neural network model, called the O-EGART-PR-FIS, is an integration of the Adaptive Resonance Theory (ART) into Generalized Regression Neural Network (GRNN), display substantial redundancy and low interpretability that leads to time-consumi...