A novel T-S fuzzy systems identification with block structured sparse representation

DOI10.1016/j.jfranklin.2013.05.008zbMath1290.93109OpenAlexW1995675008MaRDI QIDQ2017275

Zhijun Li, Fuchun Sun, Huaping Liu, Minnan Luo

Publication date: 25 June 2014

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2013.05.008

zbMATH Keywords

identificationblock structured sparse representationfuzzy partition based T-S fuzzy systemsgeometrical structure of input variables

Mathematics Subject Classification ID

Fuzzy control/observation systems (93C42) System identification (93B30)

Related Items (7)

Functional observer based controller for stabilizing Takagi-Sugeno fuzzy systems with time-delays ⋮ Ensemble extreme learning machine and sparse representation classification ⋮ A clustering algorithm based TS fuzzy model for tracking dynamical system data ⋮ Variable structure T-S fuzzy model and its application in maneuvering target tracking ⋮ Identification and simplification of T-S fuzzy neural networks based on incremental structure learning and similarity analysis ⋮ Analysis of the self projected matching pursuit algorithm ⋮ A novel identification method for Takagi-Sugeno fuzzy model

Cites Work

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• A fuzzy hybrid hierarchical clustering method with a new criterion able to find the optimal partition
• The composite absolute penalties family for grouped and hierarchical variable selection
• Improved fuzzy partitions for fuzzy regression models
• Partition validity and defuzzification
• On the use of the weighted fuzzy c-means in fuzzy modeling
• T-S fuzzy control for dithered nonlinear singularly perturbed systems with multiple time delays
• Stabilization via extended nonquadratic boundedness for constrained nonlinear systems in Takagi-Sugeno's form
• Building an interpretable fuzzy rule base from data using Orthogonal Least Squares - Application to a depollution problem
• Extensions of vector quantization for incremental clustering
• Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction
• Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
• Sparse and Redundant Representations
• Model Selection and Estimation in Regression with Grouped Variables

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