NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches

DOI10.1016/J.FSS.2004.09.015zbMath1058.62077OpenAlexW1975936518MaRDI QIDQ1770732

Xianqiang Yang

Publication date: 7 April 2005

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2004.09.015

zbMATH Keywords

Fuzzy neural networksTime series predictionNARMAX modelsTakagi-Sugeno-Kang fuzzy inference systems

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Fuzzy control/observation systems (93C42) Neural nets and related approaches to inference from stochastic processes (62M45)

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Cites Work

• An orthogonal least-squares method for recurrent fuzzy-neural modeling.
• Temporal problems solved by dynamic fuzzy network based on genetic algorithm with variable-length chromosomes.
• Identification and prediction using recurrent compensatory neuro-fuzzy systems
• A load curve based fuzzy modeling technique for short-term load forecasting
• Improved RAN sequential prediction using orthogonal techniques
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