Dynamic T-S Fuzzy Systems Identification Based on Sparse Regularization

DOI10.1002/asjc.890zbMath1332.93208OpenAlexW1563821794MaRDI QIDQ2789921

Minnan Luo, Huaping Liu, Fuchun Sun

Publication date: 2 March 2016

Published in: Asian Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/asjc.890

zbMATH Keywords

sparse representationfuzzy rules reductionfuzzy systems identification

Mathematics Subject Classification ID

Fuzzy control/observation systems (93C42) System identification (93B30) Least squares and related methods for stochastic control systems (93E24) Identification in stochastic control theory (93E12)

Related Items (3)

Recursive Generalized Maximum Correntropy Criterion Algorithm with Sparse Penalty Constraints for System Identification ⋮ A novel identification method for Takagi-Sugeno fuzzy model ⋮ Fault Diagnosis and Sliding Mode Fault Tolerant Control for Non‐Gaussian Stochastic Distribution Control Systems Using T‐s Fuzzy Model

Uses Software

• ANFIS
• SparseFIS

Cites Work

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