Recurrent fuzzy-neural approach for nonlinear control using dynamic structure learning scheme

Neurocomputing

Volumn 71, Issue 16-18, 2008, Pages 3447-3459

  Hsu, Chun Fei ^a   Cheng, Kuo Hsiang ^b  

^a CHUNG HUA UNIVERSITY   (Taiwan)

^b INDUSTRIAL TECHNOLOGY RESEARCH INSTITUTE   (Taiwan)

Author keywords

Dynamic node construction; Nonlinear control; Recurrent fuzzy neural network

Indexed keywords

CHAOTIC SYSTEMS; CONTROL THEORY; DIFFERENTIAL EQUATIONS; EDUCATION; FUZZY CONTROL; FUZZY NEURAL NETWORKS; IMAGE CLASSIFICATION; LYAPUNOV FUNCTIONS; NONLINEAR CONTROL SYSTEMS; NONLINEAR SYSTEMS; SYSTEM STABILITY; VEGETATION;

COMPENSATION CONTROLLERS; DYNAMIC NODE CONSTRUCTION; DYNAMIC STRUCTURES; LEARNING PHASES; NEURAL CONTROLLERS; NONLINEAR CONTROL; NONLINEAR CONTROLS; OPTIMAL NETWORK STRUCTURES; RECURRENT FUZZY NEURAL NETWORK; RECURRENT FUZZY NEURAL NETWORKS; SIMULATION RESULTS; STRUCTURE LEARNING SCHEMES;

NEURAL NETWORKS;

ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; CONFERENCE PAPER; CONTROLLED STUDY; FEASIBILITY STUDY; FUZZY SYSTEM; INTERMETHOD COMPARISON; LEARNING ALGORITHM; MATHEMATICAL ANALYSIS; NONLINEAR SYSTEM; PRIORITY JOURNAL; VALIDATION PROCESS;

EID: 56549101217     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.10.014     Document Type: Conference Paper

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