A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling

IEEE Transactions on Fuzzy Systems

Volumn 18, Issue 2, 2010, Pages 261-273

  Juang, Chia Feng ^a   Hsieh, Cheng Da ^a,b  

^a NATIONAL CHUNG HSING UNIVERSITY   (Taiwan)

^b HSIUPING INSTITUTE OF TECHNOLOGY   (Taiwan)

Author keywords

Dynamic system identification; Recurrent fuzzy neural networks (FNNs); Recurrent fuzzy systems; Support vector regression (SVR)

Indexed keywords

DYNAMIC SYSTEM IDENTIFICATION; DYNAMIC SYSTEMS; FEED-BACK LOOP; FIRING STRENGTH; FREE PARAMETERS; FUZZY MODELS; GENERALIZATION ABILITY; HIDDEN LAYERS; IDENTIFICATION PROBLEM; INPUT AND OUTPUTS; LINEAR FUNCTIONS; NETWORK NODE; NOISY DATA; ONE-PASS; PARAMETER LEARNING; RECURRENT FUZZY NEURAL NETWORK; RECURRENT FUZZY SYSTEMS; RECURRENT NETWORKS; SIMULATION RESULT; STRUCTURE-LEARNING; SUPPORT VECTOR REGRESSIONS; SYSTEM MODELING; TEMPORAL PROPERTY; TRAINING DATA;

CLUSTERING ALGORITHMS; DYNAMIC PROGRAMMING; DYNAMICAL SYSTEMS; FUZZY CONTROL; FUZZY SYSTEMS; IDENTIFICATION (CONTROL SYSTEMS); LEARNING ALGORITHMS; RECURRENT NEURAL NETWORKS; REGRESSION ANALYSIS; VECTORS;

FUZZY NEURAL NETWORKS;

EID: 77950655845     PISSN: 10636706     EISSN: None     Source Type: Journal    
DOI: 10.1109/TFUZZ.2010.2040185     Document Type: Article

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