Nonlinear systems identification using dynamic multi-time scale neural networks

Neurocomputing

Volumn 74, Issue 17, 2011, Pages 3428-3439

  Han, Xuan ^a   Xie, Wen Fang ^a   Fu, Zhijun ^a   Luo, Weidong ^a  

^a Dept of Electrical and Computer Engineering   (Canada)

Author keywords

Dynamic multi time scale neural networks; Neural network identifiers; Nonlinear systems; On line identification

Indexed keywords

ACTUAL SYSTEM; DEAD-ZONE; DIFFERENT TIME SCALE; DYNAMIC NEURAL NETWORKS; IDENTIFICATION METHOD; MULTI-TIME SCALE; NEURAL NETWORK IDENTIFIERS; NEURON NETWORKS; NONLINEAR SYSTEMS IDENTIFICATION; ON-LINE IDENTIFICATION; OUTPUT SIGNAL; SINGULARLY PERTURBED; STATE VARIABLES; THEORETICAL RESULT;

ALGORITHMS; CONTROL EQUIPMENT; IDENTIFICATION (CONTROL SYSTEMS); LYAPUNOV FUNCTIONS; NONLINEAR SYSTEMS; PERTURBATION TECHNIQUES; TELECOMMUNICATION NETWORKS; TIME MEASUREMENT;

NEURAL NETWORKS;

ACCURACY; ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION ALGORITHM; INTERMETHOD COMPARISON; LINEAR SYSTEM; MATHEMATICAL MODEL; NONLINEAR SYSTEM; ONLINE SYSTEM; PRIORITY JOURNAL; PROCESS DEVELOPMENT; PROCESS MODEL; SIGNAL PROCESSING; TIME SERIES ANALYSIS;

EID: 80052934366     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.06.007     Document Type: Article

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