Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling

DOI10.1016/j.fss.2006.09.002zbMath1110.93030OpenAlexW2034922207MaRDI QIDQ868300

Kuo-Hsiang Cheng, Chunshien Li

Publication date: 2 March 2007

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

Full work available at URL: https://doi.org/10.1016/j.fss.2006.09.002

zbMATH Keywords

system identificationmodelinghybrid learning (HL)least square estimation (LSE)random optimization (RO)recurrent neuro-fuzzy system (RNFS)

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Fuzzy control/observation systems (93C42) Stochastic learning and adaptive control (93E35)

Related Items (6)

Qualitative modeling of dynamical systems employing continuous-time recurrent fuzzy systems ⋮ Robust digital design of continuous-time nonlinear control systems using adaptive prediction and random-local-optimal NARMAX model ⋮ Improving prediction models applied in systems monitoring natural hazards and machinery ⋮ Modeling with discrete-time recurrent fuzzy systems via mixed-integer optimization ⋮ ANNEALED CHAOTIC LEARNING FOR TIME SERIES PREDICTION IN IMPROVED NEURO-FUZZY NETWORK WITH FEEDBACKS ⋮ Incremental learning of dynamic fuzzy neural networks for accurate system modeling

Uses Software

• ANFIS

Cites Work

• Neuro-fuzzy systems for function approximation
• Multilayer feedforward networks are universal approximators
• A hierarchical fuzzy-clustering approach to fuzzy modeling
• Identification and prediction using recurrent compensatory neuro-fuzzy systems
• Random optimization
• Fuzzy identification of systems and its applications to modeling and control
• Prediction of Chaotic Time Series Based on the Recurrent Predictor Neural Network
• Unnamed Item

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